{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# FSRS4Anki Optimizer mini-batch punish pls"
   ]
  },
  {
   "attachments": {},
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "[![open in colab](https://colab.research.google.com/assets/colab-badge.svg)](https://colab.research.google.com/github/open-spaced-repetition/fsrs4anki/blob/main/archive/experiment/mini-batch_punish_pls.ipynb)\n",
    "\n",
    "↑ Click the above button to open the optimizer on Google Colab.\n",
    "\n",
    "> If you can't see the button and are located in the Chinese Mainland, please use a proxy or VPN."
   ]
  },
  {
   "attachments": {},
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Upload your **Anki Deck Package (.apkg)** file or **Anki Collection Package (.colpkg)** file on the `Left sidebar -> Files`, drag and drop your file in the current directory (not the `sample_data` directory). \n",
    "\n",
    "No need to include media. Need to include scheduling information. \n",
    "\n",
    "> If you use the latest version of Anki, please check the box `Support older Anki versions (slower/larger files)` when you export.\n",
    "\n",
    "You can export it via `File -> Export...` or `Ctrl + E` in the main window of Anki.\n",
    "\n",
    "Then replace the `filename` with yours in the next code cell. And set the `timezone` and `next_day_starts_at` which can be found in your preferences of Anki.\n",
    "\n",
    "After that, just run all (`Runtime -> Run all` or `Ctrl + F9`) and wait for minutes. You can see the optimal parameters in section **2.3 Result**. Copy them, replace the parameters in `fsrs4anki_scheduler.js`, and paste them into the custom scheduling of your deck options (require Anki version >= 2.1.55).\n",
    "\n",
    "**NOTE**: The default output is generated from my review logs. If you find the output is the same as mine, maybe your notebook hasn't run there.\n",
    "\n",
    "**Contribute to SRS Research**: If you want to share your data with me, please fill this form: https://forms.gle/KaojsBbhMCytaA7h8"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Here are some settings that you need to replace before running this optimizer.\n",
    "\n",
    "filename = \"../collection-2022-09-18@13-21-58.colpkg\"\n",
    "# If you upload deck file, replace it with your deck filename. E.g., ALL__Learning.apkg\n",
    "# If you upload collection file, replace it with your colpgk filename. E.g., collection-2022-09-18@13-21-58.colpkg\n",
    "\n",
    "# Replace it with your timezone. I'm in China, so I use Asia/Shanghai.\n",
    "# You can find your timezone here: https://gist.github.com/heyalexej/8bf688fd67d7199be4a1682b3eec7568\n",
    "timezone = 'Asia/Shanghai'\n",
    "\n",
    "# Replace it with your Anki's setting in Preferences -> Scheduling.\n",
    "next_day_starts_at = 4\n",
    "\n",
    "# Replace it if you don't want the optimizer to use the review logs before a specific date.\n",
    "revlog_start_date = \"2006-10-05\""
   ]
  },
  {
   "attachments": {},
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## 1 Build dataset"
   ]
  },
  {
   "attachments": {},
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### 1.1 Extract Anki collection & deck file"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Extract successfully!\n"
     ]
    }
   ],
   "source": [
    "import zipfile\n",
    "import sqlite3\n",
    "import time\n",
    "from tqdm import notebook\n",
    "import pandas as pd\n",
    "import numpy as np\n",
    "import os\n",
    "from datetime import timedelta, datetime\n",
    "import matplotlib.pyplot as plt\n",
    "import math\n",
    "import torch\n",
    "from torch import nn\n",
    "import warnings\n",
    "\n",
    "notebook.tqdm.pandas()\n",
    "warnings.filterwarnings(\"ignore\", category=UserWarning)\n",
    "\n",
    "\n",
    "# Extract the collection file or deck file to get the .anki21 database.\n",
    "with zipfile.ZipFile(f'./{filename}', 'r') as zip_ref:\n",
    "    zip_ref.extractall('./')\n",
    "    print(\"Extract successfully!\")\n"
   ]
  },
  {
   "attachments": {},
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### 1.2 Create time-series feature & analysis\n",
    "\n",
    "The following code cell will extract the review logs from your Anki collection and preprocess them to a trainset which is saved in [./revlog_history.tsv](./revlog_history.tsv).\n",
    "\n",
    "The time-series features are important in optimizing the model's parameters. For more detail, please see my paper: https://www.maimemo.com/paper/\n",
    "\n",
    "Then it will generate a concise analysis for your review logs. \n",
    "\n",
    "- The `r_history` is the history of ratings on each review. `3,3,3,1` means that you press `Good, Good, Good, Again`. It only contains the first rating for each card on the review date, i.e., when you press `Again` in review and  `Good` in relearning steps 10min later, only `Again` will be recorded.\n",
    "- The `avg_interval` is the actual average interval after you rate your cards as the `r_history`. It could be longer than the interval given by Anki's built-in scheduler because you reviewed some overdue cards.\n",
    "- The `avg_retention` is the average retention after you press as the `r_history`. `Again` counts as failed recall, and `Hard, Good and Easy` count as successful recall. Retention is the percentage of your successful recall.\n",
    "- The `stability` is the estimated memory state variable, which is an approximate interval that leads to 90% retention.\n",
    "- The `factor` is `stability / previous stability`.\n",
    "- The `group_cnt` is the number of review logs that have the same `r_history`."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "revlog.csv saved.\n",
      "Trainset saved.\n",
      "Retention calculated.\n"
     ]
    },
    {
     "data": {
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       "model_id": "ed7ec7662f2f4e2994ed04764d40f38b",
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       "version_minor": 0
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     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Stability calculated.\n"
     ]
    },
    {
     "data": {
      "application/vnd.jupyter.widget-view+json": {
       "model_id": "d7c5bb54194d4baf886ff19c2b01eb86",
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     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "1:again, 2:hard, 3:good, 4:easy\n",
      "\n",
      "      r_history  avg_interval  avg_retention  stability  factor  group_cnt\n",
      "              1           1.7          0.765        1.0     inf       7997\n",
      "            1,3           3.9          0.877        4.2    4.20       4174\n",
      "          1,3,3           8.5          0.882        9.1    2.17       2711\n",
      "        1,3,3,3          17.8          0.860       13.8    1.52       1619\n",
      "      1,3,3,3,3          36.4          0.833       22.3    1.62        845\n",
      "    1,3,3,3,3,3          74.7          0.861       36.4    1.63        402\n",
      "  1,3,3,3,3,3,3         118.2          0.895       38.5    1.06        182\n",
      "              2           1.0          0.901        1.1     inf        240\n",
      "            2,3           3.5          0.946        8.3    7.55        201\n",
      "          2,3,3          11.1          0.890        7.1    0.86        160\n",
      "              3           1.5          0.962        5.4     inf       9134\n",
      "            3,3           3.9          0.966       15.2    2.81       6588\n",
      "          3,3,3           9.0          0.960       23.5    1.55       5166\n",
      "        3,3,3,3          19.1          0.941       43.5    1.85       3542\n",
      "      3,3,3,3,3          35.9          0.931       53.0    1.22       1976\n",
      "    3,3,3,3,3,3          65.0          0.928       97.6    1.84       1149\n",
      "  3,3,3,3,3,3,3         100.9          0.947      142.9    1.46        537\n",
      "3,3,3,3,3,3,3,3         133.6          0.990     1821.9   12.75        131\n",
      "              4           3.8          0.966       12.1     inf      11599\n",
      "            4,3           8.1          0.975       39.0    3.22       7515\n",
      "          4,3,3          17.8          0.963       56.5    1.45       5307\n",
      "        4,3,3,3          32.1          0.952       82.6    1.46       3032\n",
      "      4,3,3,3,3          46.3          0.956      125.4    1.52       1380\n",
      "    4,3,3,3,3,3          67.5          0.956      115.3    0.92        534\n",
      "  4,3,3,3,3,3,3          78.3          0.978      117.5    1.02        253\n",
      "4,3,3,3,3,3,3,3         114.1          0.984      177.8    1.51        166\n",
      "Analysis saved!\n"
     ]
    }
   ],
   "source": [
    "if os.path.isfile(\"collection.anki21b\"):\n",
    "    os.remove(\"collection.anki21b\")\n",
    "    raise Exception(\n",
    "        \"Please export the file with `support older Anki versions` if you use the latest version of Anki.\")\n",
    "elif os.path.isfile(\"collection.anki21\"):\n",
    "    con = sqlite3.connect(\"collection.anki21\")\n",
    "elif os.path.isfile(\"collection.anki2\"):\n",
    "    con = sqlite3.connect(\"collection.anki2\")\n",
    "else:\n",
    "    raise Exception(\"Collection not exist!\")\n",
    "cur = con.cursor()\n",
    "res = cur.execute(\"SELECT * FROM revlog\")\n",
    "revlog = res.fetchall()\n",
    "\n",
    "df = pd.DataFrame(revlog)\n",
    "df.columns = ['id', 'cid', 'usn', 'r', 'ivl',\n",
    "              'last_lvl', 'factor', 'time', 'type']\n",
    "df = df[(df['cid'] <= time.time() * 1000) &\n",
    "        (df['id'] <= time.time() * 1000) &\n",
    "        (df['r'] > 0)].copy()\n",
    "df['create_date'] = pd.to_datetime(df['cid'] // 1000, unit='s')\n",
    "df['create_date'] = df['create_date'].dt.tz_localize(\n",
    "    'UTC').dt.tz_convert(timezone)\n",
    "df['review_date'] = pd.to_datetime(df['id'] // 1000, unit='s')\n",
    "df['review_date'] = df['review_date'].dt.tz_localize(\n",
    "    'UTC').dt.tz_convert(timezone)\n",
    "df.drop(df[df['review_date'].dt.year < 2006].index, inplace=True)\n",
    "df.sort_values(by=['cid', 'id'], inplace=True, ignore_index=True)\n",
    "type_sequence = np.array(df['type'])\n",
    "time_sequence = np.array(df['time'])\n",
    "df.to_csv(\"revlog.csv\", index=False)\n",
    "print(\"revlog.csv saved.\")\n",
    "df = df[(df['type'] != 3) | (df['factor'] != 0)].copy()\n",
    "df['real_days'] = df['review_date'] - timedelta(hours=next_day_starts_at)\n",
    "df['real_days'] = pd.DatetimeIndex(df['real_days'].dt.floor('D', ambiguous='infer', nonexistent='shift_forward')).to_julian_date()\n",
    "df.drop_duplicates(['cid', 'real_days'], keep='first', inplace=True)\n",
    "df['delta_t'] = df.real_days.diff()\n",
    "df.dropna(inplace=True)\n",
    "df['delta_t'] = df['delta_t'].astype(dtype=int)\n",
    "\n",
    "df['i'] = df.groupby('cid').cumcount() + 1\n",
    "df.loc[df['i'] == 1, 'delta_t'] = 0\n",
    "\n",
    "\n",
    "df = df.groupby('cid').filter(lambda group: group['type'].iloc[0] == 0)\n",
    "df['prev_type'] = df.groupby('cid')['type'].shift(1).fillna(0).astype(int)\n",
    "df['helper'] = (df['type'] == 0) & ((df['prev_type'] == 1) | (df['prev_type'] == 2)) & (df['i'] > 1)\n",
    "df['helper'] = df['helper'].astype(int)\n",
    "df['helper'] = df.groupby('cid')['helper'].cumsum()\n",
    "df = df[df['helper'] == 0]\n",
    "del df['prev_type']\n",
    "del df['helper']\n",
    "from itertools import accumulate\n",
    "\n",
    "def cum_concat(x):\n",
    "    return list(accumulate(x))\n",
    "\n",
    "t_history = df.groupby('cid', group_keys=False)['delta_t'].apply(lambda x: cum_concat([[i] for i in x]))\n",
    "df['t_history']=[','.join(map(str, item[:-1])) for sublist in t_history for item in sublist]\n",
    "r_history = df.groupby('cid', group_keys=False)['r'].apply(lambda x: cum_concat([[i] for i in x]))\n",
    "df['r_history']=[','.join(map(str, item[:-1])) for sublist in r_history for item in sublist]\n",
    "df = df[df['id'] >= time.mktime(datetime.strptime(revlog_start_date, \"%Y-%m-%d\").timetuple()) * 1000]\n",
    "df.to_csv('revlog_history.tsv', sep=\"\\t\", index=False)\n",
    "print(\"Trainset saved.\")\n",
    "\n",
    "df['y'] = df['r'].map(lambda x: {1: 0, 2: 1, 3: 1, 4: 1}[x])\n",
    "df['retention'] = df.groupby(by=['r_history', 'delta_t'], group_keys=False)['y'].transform('mean')\n",
    "df['total_cnt'] = df.groupby(by=['r_history', 'delta_t'], group_keys=False)['id'].transform('count')\n",
    "print(\"Retention calculated.\")\n",
    "df = df.drop(columns=['id', 'cid', 'usn', 'ivl', 'last_lvl', 'factor', 'time', 'type', 'create_date', 'review_date', 'real_days', 'r', 't_history', 'y'])\n",
    "df.drop_duplicates(inplace=True)\n",
    "df['retention'] = df['retention'].map(lambda x: max(min(0.99, x), 0.01))\n",
    "\n",
    "def cal_stability(group: pd.DataFrame) -> pd.DataFrame:\n",
    "    group_cnt = sum(group['total_cnt'])\n",
    "    if group_cnt < 10:\n",
    "        return pd.DataFrame()\n",
    "    group['group_cnt'] = group_cnt\n",
    "    if group['i'].values[0] > 1:\n",
    "        r_ivl_cnt = sum(group['delta_t'] * group['retention'].map(np.log) * pow(group['total_cnt'], 2))\n",
    "        ivl_ivl_cnt = sum(group['delta_t'].map(lambda x: x ** 2) * pow(group['total_cnt'], 2))\n",
    "        group['stability'] = round(np.log(0.9) / (r_ivl_cnt / ivl_ivl_cnt), 1)\n",
    "    else:\n",
    "        group['stability'] = 0.0\n",
    "    group['avg_retention'] = round(sum(group['retention'] * pow(group['total_cnt'], 2)) / sum(pow(group['total_cnt'], 2)), 3)\n",
    "    group['avg_interval'] = round(sum(group['delta_t'] * pow(group['total_cnt'], 2)) / sum(pow(group['total_cnt'], 2)), 1)\n",
    "    del group['total_cnt']\n",
    "    del group['retention']\n",
    "    del group['delta_t']\n",
    "    return group\n",
    "\n",
    "df = df.groupby(by=['r_history'], group_keys=False).progress_apply(cal_stability)\n",
    "print(\"Stability calculated.\")\n",
    "df.reset_index(drop = True, inplace = True)\n",
    "df.drop_duplicates(inplace=True)\n",
    "df.sort_values(by=['r_history'], inplace=True, ignore_index=True)\n",
    "\n",
    "if df.shape[0] > 0:\n",
    "    for idx in notebook.tqdm(df.index):\n",
    "        item = df.loc[idx]\n",
    "        index = df[(df['i'] == item['i'] + 1) & (df['r_history'].str.startswith(item['r_history']))].index\n",
    "        df.loc[index, 'last_stability'] = item['stability']\n",
    "    df['factor'] = round(df['stability'] / df['last_stability'], 2)\n",
    "    df = df[(df['i'] >= 2) & (df['group_cnt'] >= 100)]\n",
    "    df['last_recall'] = df['r_history'].map(lambda x: x[-1])\n",
    "    df = df[df.groupby(['i', 'r_history'], group_keys=False)['group_cnt'].transform(max) == df['group_cnt']]\n",
    "    df.to_csv('./stability_for_analysis.tsv', sep='\\t', index=None)\n",
    "    print(\"1:again, 2:hard, 3:good, 4:easy\\n\")\n",
    "    print(df[df['r_history'].str.contains(r'^[1-4][^124]*$', regex=True)][['r_history', 'avg_interval', 'avg_retention', 'stability', 'factor', 'group_cnt']].to_string(index=False))\n",
    "    print(\"Analysis saved!\")"
   ]
  },
  {
   "attachments": {},
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## 2 Optimize parameter"
   ]
  },
  {
   "attachments": {},
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### 2.1 Define the model\n",
    "\n",
    "FSRS is a time-series model for predicting memory states."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [],
   "source": [
    "init_w = [1, 1, 5, 0.5, 0.5, 0.2, 1.4, 0.2, 0.8, 2, 0.2, 0.2, 1]\n",
    "'''\n",
    "w[0]: initial_stability_for_again_answer\n",
    "w[1]: initial_stability_step_per_rating\n",
    "w[2]: initial_difficulty_for_good_answer\n",
    "w[3]: initial_difficulty_step_per_rating\n",
    "w[4]: next_difficulty_step_per_rating\n",
    "w[5]: next_difficulty_reversion_to_mean_speed (used to avoid ease hell)\n",
    "w[6]: next_stability_factor_after_success\n",
    "w[7]: next_stability_stabilization_decay_after_success\n",
    "w[8]: next_stability_retrievability_gain_after_success\n",
    "w[9]: next_stability_factor_after_failure\n",
    "w[10]: next_stability_difficulty_decay_after_success\n",
    "w[11]: next_stability_stability_gain_after_failure\n",
    "w[12]: next_stability_retrievability_gain_after_failure\n",
    "For more details about the parameters, please see: \n",
    "https://github.com/open-spaced-repetition/fsrs4anki/wiki/Free-Spaced-Repetition-Scheduler\n",
    "'''\n",
    "\n",
    "\n",
    "class FSRS(nn.Module):\n",
    "    def __init__(self, w):\n",
    "        super(FSRS, self).__init__()\n",
    "        self.w = nn.Parameter(torch.tensor(w))\n",
    "\n",
    "    def stability_after_success(self, state, new_d, r):\n",
    "        new_s = state[:,0] * (1 + torch.exp(self.w[6]) *\n",
    "                        (11 - new_d) *\n",
    "                        torch.pow(state[:,0], -self.w[7]) *\n",
    "                        (torch.exp((1 - r) * self.w[8]) - 1))\n",
    "        return new_s\n",
    "    \n",
    "    def stability_after_failure(self, state, new_d, r):\n",
    "        new_s = self.w[9] * torch.pow(new_d, -self.w[10]) * torch.pow(\n",
    "            state[:,0], self.w[11]) * torch.exp((1 - r) * self.w[12])\n",
    "        return new_s\n",
    "\n",
    "    def step(self, X, state):\n",
    "        '''\n",
    "        :param X: shape[batch_size, 2], X[:,0] is elapsed time, X[:,1] is rating\n",
    "        :param state: shape[batch_size, 2], state[:,0] is stability, state[:,1] is difficulty\n",
    "        :return state:\n",
    "        '''\n",
    "        if torch.equal(state, torch.zeros_like(state)):\n",
    "            # first learn, init memory states\n",
    "            new_s = self.w[0] + self.w[1] * (X[:,1] - 1)\n",
    "            new_d = self.w[2] - self.w[3] * (X[:,1] - 3)\n",
    "            new_d = new_d.clamp(1, 10)\n",
    "        else:\n",
    "            r = torch.exp(np.log(0.9) * X[:,0] / state[:,0])\n",
    "            new_d = state[:,1] - self.w[4] * (X[:,1] - 3)\n",
    "            new_d = self.mean_reversion(self.w[2], new_d)\n",
    "            new_d = new_d.clamp(1, 10)\n",
    "            condition = X[:,1] > 1\n",
    "            new_s = torch.where(condition, self.stability_after_success(state, new_d, r), self.stability_after_failure(state, new_d, r))\n",
    "        new_s = new_s.clamp(0.1, 36500)\n",
    "        return torch.stack([new_s, new_d], dim=1)\n",
    "\n",
    "    def forward(self, inputs, state=None):\n",
    "        '''\n",
    "        :param inputs: shape[seq_len, batch_size, 2]\n",
    "        '''\n",
    "        if state is None:\n",
    "            state = torch.zeros((inputs.shape[1], 2))\n",
    "        else:\n",
    "            state, = state\n",
    "        outputs = []\n",
    "        for X in inputs:\n",
    "            state = self.step(X, state)\n",
    "            outputs.append(state)\n",
    "        return torch.stack(outputs), state\n",
    "\n",
    "    def mean_reversion(self, init, current):\n",
    "        return self.w[5] * init + (1-self.w[5]) * current\n",
    "\n",
    "\n",
    "class WeightClipper(object):\n",
    "    def __init__(self, frequency=1):\n",
    "        self.frequency = frequency\n",
    "\n",
    "    def __call__(self, module):\n",
    "        if hasattr(module, 'w'):\n",
    "            w = module.w.data\n",
    "            w[0] = w[0].clamp(0.1, 10)\n",
    "            w[1] = w[1].clamp(0.1, 5)\n",
    "            w[2] = w[2].clamp(1, 10)\n",
    "            w[3] = w[3].clamp(0.1, 5)\n",
    "            w[4] = w[4].clamp(0.1, 5)\n",
    "            w[5] = w[5].clamp(0.05, 0.5)\n",
    "            w[6] = w[6].clamp(0, 2)\n",
    "            w[7] = w[7].clamp(0.15, 0.8)\n",
    "            w[8] = w[8].clamp(0.01, 1.5)\n",
    "            w[9] = w[9].clamp(0.5, 5)\n",
    "            w[10] = w[10].clamp(0.01, 2)\n",
    "            w[11] = w[11].clamp(0.01, 0.9)\n",
    "            w[12] = w[12].clamp(0.01, 2)\n",
    "            module.w.data = w\n",
    "\n",
    "def lineToTensor(line):\n",
    "    ivl = line[0].split(',')\n",
    "    response = line[1].split(',')\n",
    "    tensor = torch.zeros(len(response), 2)\n",
    "    for li, response in enumerate(response):\n",
    "        tensor[li][0] = int(ivl[li])\n",
    "        tensor[li][1] = int(response)\n",
    "    return tensor\n",
    "\n",
    "from torch.utils.data import Dataset, DataLoader, Sampler\n",
    "from torch.nn.utils.rnn import pad_sequence, pack_padded_sequence, pad_packed_sequence\n",
    "from typing import List\n",
    "\n",
    "class RevlogDataset(Dataset):\n",
    "    def __init__(self, dataframe):\n",
    "        padded = pad_sequence(dataframe['tensor'].to_list(), batch_first=True, padding_value=0)\n",
    "        self.x_train = padded.int()\n",
    "        self.t_train = torch.tensor(dataframe['delta_t'].values, dtype=torch.int)\n",
    "        self.y_train = torch.tensor(dataframe['y'].values, dtype=torch.float)\n",
    "        self.seq_len = torch.tensor(dataframe['tensor'].map(len).values, dtype=torch.long)\n",
    "\n",
    "    def __getitem__(self, idx):\n",
    "        return self.x_train[idx], self.t_train[idx], self.y_train[idx], self.seq_len[idx]\n",
    "\n",
    "    def __len__(self):\n",
    "        return len(self.y_train)\n",
    "    \n",
    "class RevlogSampler(Sampler[List[int]]):\n",
    "    def __init__(self, data_source, batch_size):\n",
    "        self.data_source = data_source\n",
    "        self.batch_size = batch_size\n",
    "        lengths = np.array(data_source.seq_len)\n",
    "        indices = np.argsort(lengths)\n",
    "        full_batches, remainder = divmod(indices.size, self.batch_size)\n",
    "        if full_batches > 0:\n",
    "            self.batch_indices = np.split(indices[:-remainder], full_batches)\n",
    "        else:\n",
    "            self.batch_indices = []\n",
    "        if remainder:\n",
    "            self.batch_indices.append(indices[-remainder:])\n",
    "        self.batch_nums = len(self.batch_indices)\n",
    "        # seed = int(torch.empty((), dtype=torch.int64).random_().item())\n",
    "        seed = 2023\n",
    "        self.generator = torch.Generator()\n",
    "        self.generator.manual_seed(seed)\n",
    "\n",
    "    def __iter__(self):\n",
    "        yield from (self.batch_indices[idx] for idx in torch.randperm(self.batch_nums, generator=self.generator).tolist())\n",
    "\n",
    "    def __len__(self):\n",
    "        return len(self.data_source)\n",
    "\n",
    "\n",
    "def collate_fn(batch):\n",
    "    sequences, delta_ts, labels, seq_lens = zip(*batch)\n",
    "    sequences_packed = pack_padded_sequence(torch.stack(sequences, dim=1), lengths=torch.stack(seq_lens), batch_first=False, enforce_sorted=False)\n",
    "    sequences_padded, length = pad_packed_sequence(sequences_packed, batch_first=False)\n",
    "    sequences_padded = torch.as_tensor(sequences_padded)\n",
    "    seq_lens = torch.as_tensor(length)\n",
    "    delta_ts = torch.as_tensor(delta_ts)\n",
    "    labels = torch.as_tensor(labels)\n",
    "    return sequences_padded, delta_ts, labels, seq_lens\n",
    "\n",
    "class Optimizer(object):\n",
    "    def __init__(self, train_set, test_set, n_epoch=3, lr=4e-2, batch_size=512) -> None:\n",
    "        self.model = FSRS(init_w)\n",
    "        self.optimizer = torch.optim.Adam(self.model.parameters(), lr=lr)\n",
    "        self.clipper = WeightClipper()\n",
    "        self.batch_size = batch_size\n",
    "        self.build_dataset(train_set, test_set)\n",
    "        self.n_epoch = n_epoch\n",
    "        self.batch_nums = self.next_train_data_loader.batch_sampler.batch_nums\n",
    "        self.scheduler = torch.optim.lr_scheduler.CosineAnnealingLR(self.optimizer, T_max=self.batch_nums * n_epoch)\n",
    "        self.avg_train_losses = []\n",
    "        self.avg_eval_losses = []\n",
    "        self.loss_fn = nn.BCELoss(reduction='none')\n",
    "\n",
    "    def build_dataset(self, train_set, test_set):\n",
    "        pre_train_set = train_set[train_set['i'] == 2]\n",
    "        self.pre_train_set = RevlogDataset(pre_train_set)\n",
    "        sampler = RevlogSampler(self.pre_train_set, batch_size=self.batch_size)\n",
    "        self.pre_train_data_loader = DataLoader(self.pre_train_set, batch_sampler=sampler, collate_fn=collate_fn)\n",
    "\n",
    "        next_train_set = train_set[train_set['i'] > 2]\n",
    "        self.next_train_set = RevlogDataset(next_train_set)\n",
    "        sampler = RevlogSampler(self.next_train_set, batch_size=self.batch_size)\n",
    "        self.next_train_data_loader = DataLoader(self.next_train_set, batch_sampler=sampler, collate_fn=collate_fn)\n",
    "\n",
    "        self.train_set = RevlogDataset(train_set)\n",
    "        sampler = RevlogSampler(self.train_set, batch_size=self.batch_size)\n",
    "        self.train_data_loader = DataLoader(self.train_set, batch_sampler=sampler, collate_fn=collate_fn)\n",
    "\n",
    "        self.test_set = RevlogDataset(test_set)\n",
    "        sampler = RevlogSampler(self.test_set, batch_size=self.batch_size)\n",
    "        self.test_data_loader = DataLoader(self.test_set, batch_sampler=sampler, collate_fn=collate_fn)\n",
    "        print(\"dataset built\")\n",
    "\n",
    "    def train(self):\n",
    "        # pretrain\n",
    "        best_loss = np.inf\n",
    "        weighted_loss, w = self.eval()\n",
    "        if weighted_loss < best_loss:\n",
    "            best_loss = weighted_loss\n",
    "            best_w = w\n",
    "\n",
    "        pbar = notebook.tqdm(desc=\"pre-train\", colour=\"red\", total=len(self.pre_train_data_loader) * self.n_epoch)\n",
    "        for k in range(self.n_epoch):\n",
    "            for i, batch in enumerate(self.pre_train_data_loader):\n",
    "                self.model.train()\n",
    "                self.optimizer.zero_grad()\n",
    "                sequences, delta_ts, labels, seq_lens = batch\n",
    "                real_batch_size = seq_lens.shape[0]\n",
    "                outputs, _ = self.model(sequences)\n",
    "                stabilities = outputs[seq_lens-1, torch.arange(real_batch_size), 0]\n",
    "                retentions = torch.exp(np.log(0.9) * delta_ts / stabilities)\n",
    "                loss = self.loss_fn(retentions, labels).sum()\n",
    "                loss.backward()\n",
    "                self.optimizer.step()\n",
    "                self.model.apply(self.clipper)\n",
    "                pbar.update(n=real_batch_size)\n",
    "        pbar.close()\n",
    "\n",
    "        for name, param in self.model.named_parameters():\n",
    "            print(f\"{name}: {list(map(lambda x: round(float(x), 4),param))}\")\n",
    "\n",
    "        epoch_len = len(self.next_train_data_loader)\n",
    "        pbar = notebook.tqdm(desc=\"train\", colour=\"red\", total=epoch_len*self.n_epoch)\n",
    "        print_len = max(self.batch_nums*self.n_epoch // 10, 1)\n",
    "        for k in range(self.n_epoch):\n",
    "            weighted_loss, w = self.eval()\n",
    "            if weighted_loss < best_loss:\n",
    "                best_loss = weighted_loss\n",
    "                best_w = w\n",
    "            for i, batch in enumerate(self.next_train_data_loader):\n",
    "                self.model.train()\n",
    "                self.optimizer.zero_grad()\n",
    "                sequences, delta_ts, labels, seq_lens = batch\n",
    "                real_batch_size = seq_lens.shape[0]\n",
    "                outputs, _ = self.model(sequences)\n",
    "                stabilities = outputs[seq_lens-1, torch.arange(real_batch_size), 0]\n",
    "                retentions = torch.exp(np.log(0.9) * delta_ts / stabilities)\n",
    "                pls_flag = sequences[seq_lens-1, torch.arange(real_batch_size), 1] == 1\n",
    "                w_1 = torch.ones_like(retentions)\n",
    "                w_1[pls_flag] = 2 # increase the loss for post-lapse stability (more accurate)\n",
    "                pls_lapse_flag = pls_flag & (labels == 0)\n",
    "                w_2 = torch.ones_like(retentions)\n",
    "                w_2[pls_lapse_flag] = 2 # increase the loss for post-lapse stability when the label is forget (to unestimate the PLS)\n",
    "                loss = (self.loss_fn(retentions, labels) * w_1 * w_2).sum()\n",
    "                loss.backward()\n",
    "                for param in self.model.parameters():\n",
    "                    param.grad[:2] = torch.zeros(2)\n",
    "                self.optimizer.step()\n",
    "                self.scheduler.step()\n",
    "                self.model.apply(self.clipper)\n",
    "                pbar.update(real_batch_size)\n",
    "\n",
    "                if (k * self.batch_nums + i + 1) % print_len == 0:\n",
    "                    print(f\"iteration: {k * epoch_len + (i + 1) * self.batch_size}\")\n",
    "                    for name, param in self.model.named_parameters():\n",
    "                        print(f\"{name}: {list(map(lambda x: round(float(x), 4),param))}\")\n",
    "        pbar.close()\n",
    "\n",
    "        weighted_loss, w = self.eval()\n",
    "        if weighted_loss < best_loss:\n",
    "            best_loss = weighted_loss\n",
    "            best_w = w\n",
    "\n",
    "        return best_w\n",
    "\n",
    "    def eval(self):\n",
    "        self.model.eval()\n",
    "        with torch.no_grad():\n",
    "            sequences, delta_ts, labels, seq_lens = self.train_set.x_train, self.train_set.t_train, self.train_set.y_train, self.train_set.seq_len\n",
    "            real_batch_size = seq_lens.shape[0]\n",
    "            outputs, _ = self.model(sequences.transpose(0, 1))\n",
    "            stabilities = outputs[seq_lens-1, torch.arange(real_batch_size), 0]\n",
    "            retentions = torch.exp(np.log(0.9) * delta_ts / stabilities)\n",
    "            tran_loss = self.loss_fn(retentions, labels).mean()\n",
    "            self.avg_train_losses.append(tran_loss)\n",
    "            print(f\"Loss in trainset: {tran_loss:.4f}\")\n",
    "            \n",
    "            sequences, delta_ts, labels, seq_lens = self.test_set.x_train, self.test_set.t_train, self.test_set.y_train, self.test_set.seq_len\n",
    "            real_batch_size = seq_lens.shape[0]\n",
    "            outputs, _ = self.model(sequences.transpose(0, 1))\n",
    "            stabilities = outputs[seq_lens-1, torch.arange(real_batch_size), 0]\n",
    "            retentions = torch.exp(np.log(0.9) * delta_ts / stabilities)\n",
    "            test_loss = self.loss_fn(retentions, labels).mean()\n",
    "            self.avg_eval_losses.append(test_loss)\n",
    "            print(f\"Loss in testset: {test_loss:.4f}\")\n",
    "\n",
    "            w = list(map(lambda x: round(float(x), 4), dict(self.model.named_parameters())['w'].data))\n",
    "\n",
    "            weighted_loss = (tran_loss * len(self.train_set) + test_loss * len(self.test_set)) / (len(self.train_set) + len(self.test_set))\n",
    "\n",
    "            return weighted_loss, w\n",
    "\n",
    "    def plot(self):\n",
    "        plt.plot(self.avg_train_losses, label='train')\n",
    "        plt.plot(self.avg_eval_losses, label='test')\n",
    "        plt.xlabel('epoch')\n",
    "        plt.ylabel('loss')\n",
    "        plt.legend()\n",
    "        plt.show()"
   ]
  },
  {
   "attachments": {},
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### 2.2 Train the model\n",
    "\n",
    "The [./revlog_history.tsv](./revlog_history.tsv) generated before will be used for training the FSRS model."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "application/vnd.jupyter.widget-view+json": {
       "model_id": "21cf1d50e847447a85ca5bc2526bbbe8",
       "version_major": 2,
       "version_minor": 0
      },
      "text/plain": [
       "  0%|          | 0/224893 [00:00<?, ?it/s]"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Tensorized!\n",
      "TRAIN: 148471 TEST: 76422\n",
      "dataset built\n",
      "Loss in trainset: 0.3503\n",
      "Loss in testset: 0.3120\n"
     ]
    },
    {
     "data": {
      "application/vnd.jupyter.widget-view+json": {
       "model_id": "237bd89ebba14ff28c90c6d905ecc108",
       "version_major": 2,
       "version_minor": 0
      },
      "text/plain": [
       "pre-train:   0%|          | 0/52113 [00:00<?, ?it/s]"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "w: [1.0123, 2.1705, 5.0, 0.5, 0.5, 0.2, 1.4, 0.2, 0.8, 2.0, 0.2, 0.2, 1.0]\n"
     ]
    },
    {
     "data": {
      "application/vnd.jupyter.widget-view+json": {
       "model_id": "51ad79e2d898467f8ad4544b33ed8c5c",
       "version_major": 2,
       "version_minor": 0
      },
      "text/plain": [
       "train:   0%|          | 0/393300 [00:00<?, ?it/s]"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Loss in trainset: 0.3483\n",
      "Loss in testset: 0.3063\n",
      "iteration: 39424\n",
      "w: [1.0373, 2.1598, 4.9932, 1.895, 1.2384, 0.05, 1.5686, 0.2461, 1.0269, 1.4002, 0.8226, 0.4625, 1.0069]\n",
      "iteration: 78848\n",
      "w: [1.0373, 2.1598, 4.6835, 1.8819, 1.1028, 0.05, 1.5467, 0.2393, 1.0254, 1.65, 0.6074, 0.4069, 1.5301]\n",
      "iteration: 118272\n",
      "w: [1.0373, 2.1598, 4.5415, 1.9009, 1.0247, 0.05, 1.5701, 0.3543, 1.0614, 1.5818, 0.6836, 0.3593, 1.5473]\n",
      "Loss in trainset: 0.3377\n",
      "Loss in testset: 0.2973\n",
      "iteration: 157212\n",
      "w: [1.0373, 2.1598, 4.6164, 2.2504, 1.159, 0.05, 1.5394, 0.3115, 1.0245, 1.5541, 0.7108, 0.3994, 1.6859]\n",
      "iteration: 196636\n",
      "w: [1.0373, 2.1598, 4.4323, 1.9116, 1.291, 0.0525, 1.5676, 0.3579, 1.0568, 1.5645, 0.697, 0.4691, 1.8059]\n",
      "iteration: 236060\n",
      "w: [1.0373, 2.1598, 4.4183, 1.8685, 1.314, 0.05, 1.5762, 0.3003, 1.0494, 1.5335, 0.7176, 0.3694, 1.8075]\n",
      "Loss in trainset: 0.3365\n",
      "Loss in testset: 0.2955\n",
      "iteration: 275000\n",
      "w: [1.0373, 2.1598, 4.3973, 2.0222, 1.1652, 0.05, 1.5325, 0.3038, 0.9951, 1.5384, 0.7168, 0.3735, 1.8051]\n",
      "iteration: 314424\n",
      "w: [1.0373, 2.1598, 4.3674, 2.0461, 1.1103, 0.05, 1.5321, 0.3136, 0.9905, 1.5361, 0.7224, 0.4481, 1.8257]\n",
      "iteration: 353848\n",
      "w: [1.0373, 2.1598, 4.3427, 2.0238, 1.0717, 0.05, 1.5489, 0.3005, 1.0057, 1.526, 0.7286, 0.4129, 1.8196]\n",
      "iteration: 393272\n",
      "w: [1.0373, 2.1598, 4.343, 2.0218, 1.0765, 0.05, 1.5493, 0.2993, 1.0057, 1.5268, 0.7275, 0.4105, 1.8212]\n",
      "Loss in trainset: 0.3353\n",
      "Loss in testset: 0.2945\n"
     ]
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "TRAIN: 150549 TEST: 74344\n",
      "dataset built\n",
      "Loss in trainset: 0.3384\n",
      "Loss in testset: 0.3349\n"
     ]
    },
    {
     "data": {
      "application/vnd.jupyter.widget-view+json": {
       "model_id": "502b1126c73747cf80a69694efffff01",
       "version_major": 2,
       "version_minor": 0
      },
      "text/plain": [
       "pre-train:   0%|          | 0/59508 [00:00<?, ?it/s]"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "w: [1.0216, 2.7775, 5.0, 0.5, 0.5, 0.2, 1.4, 0.2, 0.8, 2.0, 0.2, 0.2, 1.0]\n"
     ]
    },
    {
     "data": {
      "application/vnd.jupyter.widget-view+json": {
       "model_id": "d5958bd915904ba1b23ef9e8d78150e1",
       "version_major": 2,
       "version_minor": 0
      },
      "text/plain": [
       "train:   0%|          | 0/392139 [00:00<?, ?it/s]"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Loss in trainset: 0.3339\n",
      "Loss in testset: 0.3322\n",
      "iteration: 38912\n",
      "w: [1.0776, 2.855, 5.2742, 1.9709, 1.3092, 0.05, 1.5649, 0.257, 1.0871, 1.497, 0.7528, 0.4138, 1.0524]\n",
      "iteration: 77824\n",
      "w: [1.0776, 2.8551, 4.8803, 1.7001, 1.2612, 0.05, 1.5473, 0.3783, 1.144, 1.5847, 0.6873, 0.3093, 1.3081]\n",
      "iteration: 116736\n",
      "w: [1.0776, 2.8551, 4.6985, 1.8618, 1.2098, 0.05, 1.4645, 0.1806, 1.057, 1.5712, 0.7269, 0.4312, 1.4697]\n",
      "Loss in trainset: 0.3237\n",
      "Loss in testset: 0.3200\n",
      "iteration: 155289\n",
      "w: [1.0776, 2.8551, 4.6476, 1.8702, 1.0789, 0.05, 1.4264, 0.2843, 1.0105, 1.606, 0.6851, 0.4176, 1.5551]\n",
      "iteration: 194201\n",
      "w: [1.0776, 2.8551, 4.5612, 1.9405, 1.0375, 0.05, 1.4145, 0.2614, 0.9809, 1.6004, 0.6838, 0.369, 1.5239]\n",
      "iteration: 233113\n",
      "w: [1.0776, 2.8551, 4.4577, 1.8374, 1.1174, 0.05, 1.4741, 0.221, 1.0497, 1.6069, 0.6753, 0.4129, 1.4812]\n",
      "Loss in trainset: 0.3207\n",
      "Loss in testset: 0.3172\n",
      "iteration: 271666\n",
      "w: [1.0776, 2.8551, 4.4858, 1.8876, 1.0937, 0.0538, 1.4459, 0.3141, 1.0249, 1.6453, 0.6252, 0.4011, 1.5317]\n",
      "iteration: 310578\n",
      "w: [1.0776, 2.8551, 4.4373, 1.8815, 1.0143, 0.05, 1.4444, 0.2857, 1.0172, 1.5805, 0.6875, 0.3652, 1.4686]\n",
      "iteration: 349490\n",
      "w: [1.0776, 2.8551, 4.4292, 1.8944, 1.0125, 0.05, 1.4627, 0.2789, 1.0333, 1.6048, 0.6618, 0.4067, 1.4839]\n",
      "iteration: 388402\n",
      "w: [1.0776, 2.8551, 4.4378, 1.9021, 1.0219, 0.05, 1.4561, 0.2801, 1.0261, 1.603, 0.663, 0.4026, 1.482]\n",
      "Loss in trainset: 0.3210\n",
      "Loss in testset: 0.3175\n"
     ]
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "TRAIN: 150766 TEST: 74127\n",
      "dataset built\n",
      "Loss in trainset: 0.3233\n",
      "Loss in testset: 0.3657\n"
     ]
    },
    {
     "data": {
      "application/vnd.jupyter.widget-view+json": {
       "model_id": "36388e646b5e413f808c7ea1d3e7f37b",
       "version_major": 2,
       "version_minor": 0
      },
      "text/plain": [
       "pre-train:   0%|          | 0/62199 [00:00<?, ?it/s]"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "w: [2.2441, 2.455, 5.0, 0.5, 0.5, 0.2, 1.4, 0.2, 0.8, 2.0, 0.2, 0.2, 1.0]\n"
     ]
    },
    {
     "data": {
      "application/vnd.jupyter.widget-view+json": {
       "model_id": "e0b50279aae14fb6806293eba633c9e7",
       "version_major": 2,
       "version_minor": 0
      },
      "text/plain": [
       "train:   0%|          | 0/390099 [00:00<?, ?it/s]"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Loss in trainset: 0.3191\n",
      "Loss in testset: 0.3700\n",
      "iteration: 38912\n",
      "w: [2.3014, 2.523, 5.0758, 1.7466, 1.4821, 0.05, 1.5912, 0.1738, 1.0923, 1.6266, 0.6646, 0.3608, 1.0959]\n",
      "iteration: 77824\n",
      "w: [2.3014, 2.523, 4.8658, 1.8174, 1.2462, 0.05, 1.4829, 0.4341, 1.0355, 1.6919, 0.6232, 0.3382, 1.6]\n",
      "iteration: 116736\n",
      "w: [2.3014, 2.523, 4.8335, 2.2689, 1.0156, 0.05, 1.5214, 0.3248, 1.0862, 1.6457, 0.7101, 0.4251, 1.926]\n",
      "Loss in trainset: 0.3096\n",
      "Loss in testset: 0.3615\n",
      "iteration: 155633\n",
      "w: [2.3014, 2.523, 4.5986, 2.0187, 1.0739, 0.05, 1.4506, 0.2719, 0.9928, 1.5772, 0.7867, 0.3435, 1.9295]\n",
      "iteration: 194545\n",
      "w: [2.3014, 2.523, 4.5473, 1.9815, 1.0115, 0.0673, 1.477, 0.2738, 1.019, 1.5332, 0.8241, 0.3648, 1.9904]\n",
      "iteration: 233457\n",
      "w: [2.3014, 2.523, 4.5321, 2.0913, 0.9877, 0.0532, 1.5471, 0.3401, 1.0841, 1.6179, 0.7503, 0.4705, 1.9971]\n",
      "Loss in trainset: 0.3073\n",
      "Loss in testset: 0.3594\n",
      "iteration: 272354\n",
      "w: [2.3014, 2.523, 4.5032, 1.9839, 1.0002, 0.05, 1.5118, 0.3222, 1.0397, 1.6226, 0.7394, 0.4472, 2.0]\n",
      "iteration: 311266\n",
      "w: [2.3014, 2.523, 4.502, 1.9336, 1.0349, 0.05, 1.5174, 0.3285, 1.0385, 1.6106, 0.7461, 0.4082, 1.9726]\n",
      "iteration: 350178\n",
      "w: [2.3014, 2.523, 4.5115, 1.9647, 1.0243, 0.05, 1.5122, 0.3234, 1.031, 1.5939, 0.7617, 0.408, 1.9621]\n",
      "iteration: 389090\n",
      "w: [2.3014, 2.523, 4.5038, 1.9643, 1.0197, 0.05, 1.5219, 0.3113, 1.0401, 1.5934, 0.7619, 0.4095, 1.964]\n",
      "Loss in trainset: 0.3065\n",
      "Loss in testset: 0.3585\n"
     ]
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\n",
      "Training finished!\n"
     ]
    }
   ],
   "source": [
    "dataset = pd.read_csv(\"./revlog_history.tsv\", sep='\\t', index_col=None, dtype={'r_history': str ,'t_history': str} )\n",
    "dataset = dataset[(dataset['i'] > 1) & (dataset['delta_t'] > 0) & (dataset['t_history'].str.count(',0') == 0)]\n",
    "dataset['tensor'] = dataset.progress_apply(lambda x: lineToTensor(list(zip([x['t_history']], [x['r_history']]))[0]), axis=1)\n",
    "dataset['y'] = dataset['r'].map({1: 0, 2: 1, 3: 1, 4: 1})\n",
    "dataset['group'] = dataset['r_history'] + dataset['t_history']\n",
    "print(\"Tensorized!\")\n",
    "\n",
    "from sklearn.model_selection import StratifiedGroupKFold\n",
    "\n",
    "w = []\n",
    "\n",
    "sgkf = StratifiedGroupKFold(n_splits=3)\n",
    "for train_index, test_index in sgkf.split(dataset, dataset['i'], dataset['group']):\n",
    "    print(\"TRAIN:\", len(train_index), \"TEST:\",  len(test_index))\n",
    "    train_set = dataset.iloc[train_index].copy()\n",
    "    test_set = dataset.iloc[test_index].copy()\n",
    "    optimizer = Optimizer(train_set, test_set, n_epoch=3, lr=4e-2, batch_size=512)\n",
    "    w.append(optimizer.train())\n",
    "    optimizer.plot()\n",
    "\n",
    "print(\"\\nTraining finished!\")"
   ]
  },
  {
   "attachments": {},
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### 2.3 Result\n",
    "\n",
    "Copy the optimal parameters for FSRS for you in the output of next code cell after running."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[1.4721, 2.5126, 4.445, 1.9621, 1.0688, 0.05, 1.5143, 0.3003, 1.032, 1.5804, 0.7133, 0.4071, 1.7639]\n"
     ]
    }
   ],
   "source": [
    "w = np.array(w)\n",
    "avg_w = np.round(np.mean(w, axis=0), 4)\n",
    "print(avg_w.tolist())"
   ]
  },
  {
   "attachments": {},
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### 2.4 Preview\n",
    "\n",
    "You can see the memory states and intervals generated by FSRS as if you press the good in each review at the due date scheduled by FSRS."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "1:again, 2:hard, 3:good, 4:easy\n",
      "\n",
      "first rating: 1\n",
      "rating history: 1,3,3,3,3,3,3,3,3,3,3\n",
      "interval history: 0d,1d,3d,6d,12d,21d,1.2m,1.9m,3.0m,4.5m,6.6m\n",
      "difficulty history: 0,8.4,8.2,8.0,7.8,7.6,7.5,7.3,7.2,7.0,6.9\n",
      "\n",
      "first rating: 2\n",
      "rating history: 2,3,3,3,3,3,3,3,3,3,3\n",
      "interval history: 0d,4d,10d,22d,1.4m,2.6m,4.3m,6.8m,10.4m,1.2y,1.8y\n",
      "difficulty history: 0,6.4,6.3,6.2,6.1,6.0,6.0,5.9,5.8,5.7,5.7\n",
      "\n",
      "first rating: 3\n",
      "rating history: 3,3,3,3,3,3,3,3,3,3,3\n",
      "interval history: 0d,6d,18d,1.4m,2.9m,5.3m,9.1m,1.2y,1.8y,2.7y,3.7y\n",
      "difficulty history: 0,4.4,4.4,4.4,4.4,4.4,4.4,4.4,4.4,4.4,4.4\n",
      "\n",
      "first rating: 4\n",
      "rating history: 4,3,3,3,3,3,3,3,3,3,3\n",
      "interval history: 0d,9d,28d,2.4m,5.0m,9.5m,1.4y,2.2y,3.3y,4.9y,6.9y\n",
      "difficulty history: 0,2.5,2.6,2.7,2.8,2.8,2.9,3.0,3.1,3.1,3.2\n",
      "\n"
     ]
    }
   ],
   "source": [
    "requestRetention = 0.9  # recommended setting: 0.8 ~ 0.9\n",
    "\n",
    "\n",
    "class Collection:\n",
    "    def __init__(self, w):\n",
    "        self.model = FSRS(w)\n",
    "        self.model.eval()\n",
    "\n",
    "    def predict(self, t_history, r_history):\n",
    "        with torch.no_grad():\n",
    "            line_tensor = lineToTensor(list(zip([t_history], [r_history]))[0]).unsqueeze(1)\n",
    "            output_t = self.model(line_tensor)\n",
    "            return output_t[-1][0]\n",
    "        \n",
    "    def batch_predict(self, dataset):\n",
    "        fast_dataset = RevlogDataset(dataset)\n",
    "        with torch.no_grad():\n",
    "            outputs, _ = self.model(fast_dataset.x_train.transpose(0, 1))\n",
    "            stabilities, difficulties = outputs[fast_dataset.seq_len-1, torch.arange(len(fast_dataset))].transpose(0, 1)\n",
    "            return stabilities.tolist(), difficulties.tolist()\n",
    "\n",
    "my_collection = Collection(avg_w)\n",
    "print(\"1:again, 2:hard, 3:good, 4:easy\\n\")\n",
    "for first_rating in (1,2,3,4):\n",
    "    print(f'first rating: {first_rating}')\n",
    "    t_history = \"0\"\n",
    "    d_history = \"0\"\n",
    "    r_history = f\"{first_rating}\"  # the first rating of the new card\n",
    "    # print(\"stability, difficulty, lapses\")\n",
    "    for i in range(10):\n",
    "        states = my_collection.predict(t_history, r_history)\n",
    "        # print('{0:9.2f} {1:11.2f} {2:7.0f}'.format(\n",
    "            # *list(map(lambda x: round(float(x), 4), states))))\n",
    "        next_t = max(round(float(np.log(requestRetention)/np.log(0.9) * states[0])), 1)\n",
    "        difficulty = round(float(states[1]), 1)\n",
    "        t_history += f',{int(next_t)}'\n",
    "        d_history += f',{difficulty}'\n",
    "        r_history += f\",3\"\n",
    "    print(f\"rating history: {r_history}\")\n",
    "    print(\"interval history: \" + \",\".join([f\"{ivl}d\" if ivl < 30 else f\"{ivl / 30:.1f}m\" if ivl < 365 else f\"{ivl / 365:.1f}y\" for ivl in map(int, t_history.split(','))]))\n",
    "    print(f\"difficulty history: {d_history}\")\n",
    "    print('')"
   ]
  },
  {
   "attachments": {},
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "You can change the `test_rating_sequence` to see the scheduling intervals in different ratings."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "tensor([6.4973, 4.4450])\n",
      "tensor([17.5803,  4.4450])\n",
      "tensor([42.2337,  4.4450])\n",
      "tensor([86.4493,  4.4450])\n",
      "tensor([159.4623,   4.4450])\n",
      "tensor([3.9190, 6.4757])\n",
      "tensor([0.7226, 8.4049])\n",
      "tensor([2.2430, 8.2069])\n",
      "tensor([4.5555, 8.0188])\n",
      "tensor([9.5069, 7.8401])\n",
      "tensor([17.8740,  7.6704])\n",
      "tensor([30.9386,  7.5091])\n",
      "rating history: 3,3,3,3,3,1,1,3,3,3,3,3\n",
      "interval history: 0,6,18,42,86,159,4,1,2,5,10,18,31\n",
      "difficulty history: 0,4.4,4.4,4.4,4.4,4.4,6.5,8.4,8.2,8.0,7.8,7.7,7.5\n"
     ]
    }
   ],
   "source": [
    "test_rating_sequence = \"3,3,3,3,3,1,1,3,3,3,3,3\"\n",
    "requestRetention = 0.9  # recommended setting: 0.8 ~ 0.9\n",
    "easyBonus = 1.3\n",
    "hardInterval = 1.2\n",
    "\n",
    "t_history = \"0\"\n",
    "d_history = \"0\"\n",
    "for i in range(len(test_rating_sequence.split(','))):\n",
    "    rating = test_rating_sequence[2*i]\n",
    "    last_t = int(t_history.split(',')[-1])\n",
    "    r_history = test_rating_sequence[:2*i+1]\n",
    "    states = my_collection.predict(t_history, r_history)\n",
    "    print(states)\n",
    "    next_t = max(1,round(float(np.log(requestRetention)/np.log(0.9) * states[0])))\n",
    "    if rating == '4':\n",
    "        next_t = round(next_t * easyBonus)\n",
    "    elif rating == '2':\n",
    "        next_t = round(last_t * hardInterval)\n",
    "    t_history += f',{int(next_t)}'\n",
    "    difficulty = round(float(states[1]), 1)\n",
    "    d_history += f',{difficulty}'\n",
    "print(f\"rating history: {test_rating_sequence}\")\n",
    "print(f\"interval history: {t_history}\")\n",
    "print(f\"difficulty history: {d_history}\")"
   ]
  },
  {
   "attachments": {},
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### 2.5 Predict memory states and distribution of difficulty\n",
    "\n",
    "Predict memory states for each review group and save them in [./prediction.tsv](./prediction.tsv).\n",
    "\n",
    "Meanwhile, it will count the distribution of difficulty."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "prediction.tsv saved.\n",
      "difficulty\n",
      "1     0.012206\n",
      "2     0.089945\n",
      "3     0.101506\n",
      "4     0.161041\n",
      "5     0.057770\n",
      "6     0.057307\n",
      "7     0.047098\n",
      "8     0.130418\n",
      "9     0.156292\n",
      "10    0.186418\n",
      "Name: count, dtype: float64\n"
     ]
    }
   ],
   "source": [
    "stabilities, difficulties = my_collection.batch_predict(dataset)\n",
    "stabilities = map(lambda x: round(x, 2), stabilities)\n",
    "difficulties = map(lambda x: round(x, 2), difficulties)\n",
    "dataset['stability'] = list(stabilities)\n",
    "dataset['difficulty'] = list(difficulties)\n",
    "prediction = dataset.groupby(by=['t_history', 'r_history']).agg({\"stability\": \"mean\", \"difficulty\": \"mean\", \"id\": \"count\"})\n",
    "prediction.reset_index(inplace=True)\n",
    "prediction.sort_values(by=['r_history'], inplace=True)\n",
    "prediction.rename(columns={\"id\": \"count\"}, inplace=True)\n",
    "prediction.to_csv(\"./prediction.tsv\", sep='\\t', index=None)\n",
    "print(\"prediction.tsv saved.\")\n",
    "prediction['difficulty'] = prediction['difficulty'].map(lambda x: int(round(x)))\n",
    "difficulty_distribution = prediction.groupby(by=['difficulty'])['count'].sum() / prediction['count'].sum()\n",
    "print(difficulty_distribution)\n",
    "difficulty_distribution_padding = np.zeros(10)\n",
    "for i in range(10):\n",
    "    if i+1 in difficulty_distribution.index:\n",
    "        difficulty_distribution_padding[i] = difficulty_distribution.loc[i+1]"
   ]
  },
  {
   "attachments": {},
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## 3 Optimize retention to minimize the time of reviews\n",
    "\n",
    "Calculate the optimal retention to minimize the time for long-term memory consolidation. It is an experimental feature. You can use the simulator to get more accurate results:\n",
    "\n",
    "https://github.com/open-spaced-repetition/fsrs4anki/blob/main/fsrs4anki_simulator.ipynb"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "average time for failed cards: 25.0s\n",
      "average time for recalled cards: 8.0s\n",
      "terminal stability:  876.71\n"
     ]
    },
    {
     "data": {
      "application/vnd.jupyter.widget-view+json": {
       "model_id": "bf760499b65643ec987721dd49522c13",
       "version_major": 2,
       "version_minor": 0
      },
      "text/plain": [
       "find optimal retention:   0%|          | 0/15 [00:00<?, ?it/s]"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "expected_time.csv saved.\n",
      "\n",
      "-----suggested retention (experimental): 0.85-----\n"
     ]
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "base = 1.01\n",
    "minimum_stability = 0.1\n",
    "index_offset = -(np.log(minimum_stability) / np.log(base)).round().astype(int)\n",
    "d_range = 10\n",
    "d_offset = 1\n",
    "r_time = 8\n",
    "f_time = 25\n",
    "max_time = 1e10\n",
    "\n",
    "type_block = dict()\n",
    "type_count = dict()\n",
    "type_time = dict()\n",
    "last_t = type_sequence[0]\n",
    "type_block[last_t] = 1\n",
    "type_count[last_t] = 1\n",
    "type_time[last_t] = time_sequence[0]\n",
    "for i,t in enumerate(type_sequence[1:]):\n",
    "    type_count[t] = type_count.setdefault(t, 0) + 1\n",
    "    type_time[t] = type_time.setdefault(t, 0) + time_sequence[i]\n",
    "    if t != last_t:\n",
    "        type_block[t] = type_block.setdefault(t, 0) + 1\n",
    "    last_t = t\n",
    "\n",
    "r_time = round(type_time[1]/type_count[1]/1000, 1)\n",
    "\n",
    "if 2 in type_count and 2 in type_block:\n",
    "    f_time = round(type_time[2]/type_block[2]/1000 + r_time, 1)\n",
    "\n",
    "print(f\"average time for failed cards: {f_time}s\")\n",
    "print(f\"average time for recalled cards: {r_time}s\")\n",
    "\n",
    "def stability2index(stability):\n",
    "    return (np.log(stability) / np.log(base)).round().astype(int) + index_offset\n",
    "\n",
    "def init_stability(d):\n",
    "    return max(((d - avg_w[2]) / -avg_w[3] + 2) * avg_w[1] + avg_w[0], np.power(base, -index_offset))\n",
    "\n",
    "def cal_next_recall_stability(s, r, d, response):\n",
    "    if response == 1:\n",
    "        return s * (1 + np.exp(avg_w[6]) * (11 - d) * np.power(s, -avg_w[7]) * (np.exp((1 - r) * avg_w[8]) - 1))\n",
    "    else:\n",
    "        return avg_w[9] * np.power(d, -avg_w[10]) * np.power(s, avg_w[11]) * np.exp((1 - r) * avg_w[12])\n",
    "\n",
    "terminal_stability = minimum_stability\n",
    "for _ in range(128):\n",
    "    terminal_stability = cal_next_recall_stability(terminal_stability, 0.96, 10, 1)\n",
    "index_len = stability2index(terminal_stability)\n",
    "stability_list = np.array([np.power(base, i - index_offset) for i in range(index_len)])\n",
    "print(f\"terminal stability: {stability_list.max(): .2f}\")\n",
    "df = pd.DataFrame(columns=[\"retention\", \"difficulty\", \"time\"])\n",
    "\n",
    "for percentage in notebook.tqdm(range(96, 66, -2), desc=\"find optimal retention\"):\n",
    "    recall = percentage / 100\n",
    "    time_list = np.zeros((d_range, index_len))\n",
    "    time_list[:,:-1] = max_time\n",
    "    for d in range(d_range, 0, -1):\n",
    "        s0 = init_stability(d)\n",
    "        s0_index = stability2index(s0)\n",
    "        diff = max_time\n",
    "        iteration = 0\n",
    "        while diff > 1 and iteration < 2e5:\n",
    "            iteration += 1\n",
    "            total_time = time_list[d - 1].sum()\n",
    "            s_indices = np.arange(index_len - 2, -1, -1)\n",
    "            stabilities = stability_list[s_indices]\n",
    "            intervals = np.maximum(1, np.round(stabilities * np.log(recall) / np.log(0.9)))\n",
    "            p_recalls = np.power(0.9, intervals / stabilities)\n",
    "            recall_s = cal_next_recall_stability(stabilities, p_recalls, d, 1)\n",
    "            forget_d = np.minimum(d + d_offset, 10)\n",
    "            forget_s = cal_next_recall_stability(stabilities, p_recalls, forget_d, 0)\n",
    "            recall_s_indices = np.minimum(stability2index(recall_s), index_len - 1)\n",
    "            forget_s_indices = np.clip(stability2index(forget_s), 0, index_len - 1)\n",
    "            recall_times = time_list[d - 1][recall_s_indices] + r_time\n",
    "            forget_times = time_list[forget_d - 1][forget_s_indices] + f_time\n",
    "            exp_times = p_recalls * recall_times + (1.0 - p_recalls) * forget_times\n",
    "            mask = exp_times <= time_list[d - 1][s_indices]\n",
    "            time_list[d - 1][s_indices[mask]] = exp_times[mask]\n",
    "            diff = total_time - time_list[d - 1].sum()\n",
    "            s0_time = time_list[d - 1][s0_index]\n",
    "            if iteration % 1e5 == 0:\n",
    "                print(f\"iteration: {iteration}, diff: {diff}, s0_time: {s0_time}\")\n",
    "        df.loc[0 if pd.isnull(df.index.max()) else df.index.max() + 1] = [recall, d, s0_time]\n",
    "\n",
    "df.sort_values(by=[\"difficulty\", \"retention\"], inplace=True)\n",
    "df.to_csv(\"./expected_time.csv\", index=False)\n",
    "print(\"expected_time.csv saved.\")\n",
    "\n",
    "optimal_retention_list = np.zeros(10)\n",
    "for d in range(1, d_range+1):\n",
    "    retention = df[df[\"difficulty\"] == d][\"retention\"]\n",
    "    time = df[df[\"difficulty\"] == d][\"time\"]\n",
    "    optimal_retention = retention.iat[time.argmin()]\n",
    "    optimal_retention_list[d-1] = optimal_retention\n",
    "    plt.plot(retention, time, label=f\"d={d}, r={optimal_retention}\")\n",
    "print(f\"\\n-----suggested retention (experimental): {np.inner(difficulty_distribution_padding, optimal_retention_list):.2f}-----\")\n",
    "plt.ylabel(\"expected time (second)\")\n",
    "plt.xlabel(\"retention\")\n",
    "plt.legend()\n",
    "plt.grid()\n",
    "plt.semilogy()\n",
    "plt.show()"
   ]
  },
  {
   "attachments": {},
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## 4 Evaluate the model"
   ]
  },
  {
   "attachments": {},
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### 4.1 Loss\n",
    "\n",
    "Evaluate the model with the log loss. It will compare the log loss between initial model and trained model.\n",
    "\n",
    "And it will predict the stability, difficulty and retrievability for each revlog in [./evaluation.tsv](./evaluation.tsv)."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Loss before training: 0.3373\n",
      "Loss after training: 0.3212\n"
     ]
    }
   ],
   "source": [
    "my_collection = Collection(init_w)\n",
    "stabilities, difficulties = my_collection.batch_predict(dataset)\n",
    "dataset['stability'] = stabilities\n",
    "dataset['difficulty'] = difficulties\n",
    "dataset['p'] = np.exp(np.log(0.9) * dataset['delta_t'] / dataset['stability'])\n",
    "dataset['log_loss'] = dataset.apply(lambda row: - np.log(row['p']) if row['y'] == 1 else - np.log(1 - row['p']), axis=1)\n",
    "print(f\"Loss before training: {dataset['log_loss'].mean():.4f}\")\n",
    "\n",
    "my_collection = Collection(avg_w)\n",
    "stabilities, difficulties = my_collection.batch_predict(dataset)\n",
    "dataset['stability'] = stabilities\n",
    "dataset['difficulty'] = difficulties\n",
    "dataset['p'] = np.exp(np.log(0.9) * dataset['delta_t'] / dataset['stability'])\n",
    "dataset['log_loss'] = dataset.apply(lambda row: - np.log(row['p']) if row['y'] == 1 else - np.log(1 - row['p']), axis=1)\n",
    "print(f\"Loss after training: {dataset['log_loss'].mean():.4f}\")\n",
    "\n",
    "tmp = dataset.copy()\n",
    "tmp['stability'] = tmp['stability'].map(lambda x: round(x, 2))\n",
    "tmp['difficulty'] = tmp['difficulty'].map(lambda x: round(x, 2))\n",
    "tmp['p'] = tmp['p'].map(lambda x: round(x, 2))\n",
    "tmp['log_loss'] = tmp['log_loss'].map(lambda x: round(x, 2))\n",
    "tmp.rename(columns={\"r\": \"grade\", \"p\": \"retrievability\"}, inplace=True)\n",
    "tmp[['id', 'cid', 'review_date', 'r_history', 't_history', 'delta_t', 'grade', 'stability', 'difficulty', 'retrievability', 'log_loss']].to_csv(\"./evaluation.tsv\", sep='\\t', index=False)\n",
    "del tmp"
   ]
  },
  {
   "attachments": {},
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### 4.2 Calibration graph\n",
    "\n",
    "1. FSRS predicts the stability and retention for each review.\n",
    "2. Reviews are grouped into 40 bins according to their predicted retention.\n",
    "3. Count the true retention of each bin.\n",
    "4. Plot (predicted retention, true retention) in the line graph.\n",
    "5. Plot (predicted retention, size of bin) in the bar graph.\n",
    "6. Combine these graphs to create the calibration graph.\n",
    "\n",
    "Ideally, the blue line should be aligned with the orange one. If the blue line is higher than the orange line, the FSRS underestimates the retention. When the size of reviews within a bin is small, actual retention may deviate largely, which is normal.\n",
    "\n",
    "R-squared (aka the coefficient of determination), is the proportion of the variation in the dependent variable that is predictable from the independent variable(s). The higher the R-squared, the better the model fits your data. For details, please see https://en.wikipedia.org/wiki/Coefficient_of_determination\n",
    "\n",
    "RMSE (root mean squared error) is the square root of the average of squared differences between prediction and actual observation. The lower the RMSE, the better the model fits your data. For details, please see https://en.wikipedia.org/wiki/Root-mean-square_deviation"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "R-squared: 0.7443\n",
      "RMSE: 0.0273\n",
      "[0.27570358 0.69590327]\n",
      "Last rating: 1\n",
      "R-squared: -1.0421\n",
      "RMSE: 0.0858\n",
      "[0.54874068 0.37127301]\n",
      "Last rating: 2\n",
      "R-squared: -0.0735\n",
      "RMSE: 0.0482\n",
      "[-0.02998051  0.98241886]\n",
      "Last rating: 3\n",
      "R-squared: 0.9282\n",
      "RMSE: 0.0167\n",
      "[0.07694361 0.92374331]\n",
      "Last rating: 4\n",
      "R-squared: 0.2152\n",
      "RMSE: 0.0337\n",
      "[0.34890598 0.64421847]\n"
     ]
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 640x480 with 2 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 640x480 with 2 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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qST36DZLOhYZL50LDJZVGK02ePFlq1qxZsrnv3r0rAVJISIgUFRUlOTo6SqtXrza9/uLFC8nFxUX67LPP0rwvixcvTra+pBw8eFDy9PSU4uPjk+0vVqyYNH/+/DTnLFeunPTLL79IkiRJ69atkzw9PZNdb1Lq16+f7vqMzJkzR3J1dZU8PDykhg0bSpMmTZJu3rxpen3+/PlSrly5pLi4ONO+KT/MkgDpr+0HpHOh4dIvwb+luNa///5byuhtNen1SJIkFS5cWGrXrl2yMX379pV69eol6XQ6075de/ZKcrlcOnPrSYb34VWMv1tubm6Sm5ubhCGJWmrQ9D3pXGi4pNPppX79+kkDBw5MdtzBgwcluVwuxcXFSSEhIRIgnThxwvT69evXJUD66aefTPsAafjw4cnmady4sTR16tRk+5YtWybly5dPkiRJ+vHHH6WSJUtKarU6xdoteebXrl2TAOnQoUOm158/fy65uLiYfpcXL14sAdKNGzdMY+bOnSvlzZs31fljYmKkkydPmn2vcyS3/5OkGaUkabyn9OKr3NKYH2ZLk9celSavPZbmz5g//pVevHhh2Wlu35YAKTQ0NIsuxH4QpVAEAito0KABMTExnDhxgvDwcEqWLImfnx/169enT58+xMfHs2/fPooWLUqhQoW4dOkSsbGxNG3aNNk8arWaoKCgVM9x9epV2rdvn2zfO++8w+bNm5PtCwwMTBZzlC9fvmTWlFcNknoJzp07x969e3F3d09x3ps3b6JSqVCr1dSoUcO039fXl1KlSqV/Y9Lh3LlzREdHp+hGEhcXZ0o4iY6OZsKECWzZsoVHjx6h1WqJi4szWbqaNm1K4cKFKVq0KC1atKBFixa0b9/e4kLYQ4YMoWfPnuzbt4+jR4+yZs0apk6dyqZNm2jatCkhISFUrFgxWReKoGo1ks1hjqE3o+sxUq1atWTb58+f5/z588nc9pIkodfreRh6z+r7cPDgQVxdXTl69ChTp05l7Hc/AYZyKOfOneP8+fMsX748xTlv377NtWvXcHBwoEqVxID44sWL4+OTsk7Zq9dz7tw5Dh06lMzyrNPpiI+PJzY2ls6dOzNr1izT9bRs2ZI2bdrg4OBg0bWGhITg4OCQ7Pc2V65clCpVipCQENM+V1fXZHGwr/7NCMxEr4ODM2HfVJD0kLskNPkZ50sOuHl6Z/fqcjRC3AneKFyVrkR/FZ1t5zaX4sWLU7BgQfbu3Ut4eDj169cHIH/+/AQEBHD48GH27t1Lo0aNAMOHPMCWLVsoUKBAsrmcnJwytW6lMnnrKJlMlq67Qy9JREdH06ZNG77//vvE/Qn9TEuUKMGtW7cytabUiI6OThGraMQYuzZq1Ch27drFjBkzKF68OC4uLnTq1MmUZODh4cHp06fZt28fO3fuZNy4cUyYMIETJ05YnKXr4eFBmzZtaNOmDVOmTKF58+ZMmTIlhQA3YsxWVRiL2srkKVz5ryYPZHQ9RtzckvepjY6Opnfv3nz++eem9mPRKg33w+IoFFjY6vtQpEgRvL29KVWqFE+fPmX0J31ZtHYL2oRn//HHHyeLdTNSqFAhrl27lua8r5La9UycOJEOHTqkGOvs7ExAQABXr17l33//ZdeuXXzyySf88MMP7N+/36bP3EhqfzOvPktBBkQ/hfUD4NY+w3albtByBsSo4dINi6eLiFXj6axEbos6PwIh7gRvFjKZDDfHrGvIbksaNmzIvn37CA8P53//+59pf7169di2bRvHjx9n8ODBAJQtWxYnJyfu3btnEoIZUapUKU6cOJFs36vb5qB0dEwWW6aXoEqVKqxbt47AwEAcHAxvA3q9nsjISNzc3ChWrBhKpZJjx45RqFAhwBCofu3aNbPX/ypVqlTh8ePHODg4EBgYmOqYQ4cO0bt3b5PFMjo6OkVShIODA02aNKFJkyaMHz8eb29v9uzZQ4cOHXB85VrNRSaTUbp0aQ4fPgxAmTJlWLZsGfHx8Sbr3ZmTxw3nVxg+fHx9cxEVFUVMTIxJzJw9e9bi60mNoKAgrl69SvHixU3i7mWcBrlXjKmnbHr3wRyGDBnCt1OnsnvbZop81IUqVapw+fJlihcvnur4UqVKodVqOXPmDFWrVgXgxo0bhIeHZ3iuKlWqmK4nLVxcXExie8iQIZQuXZoLFy5QpUoVs6+1TJkyaLVajh07Rq1atQB48eIFV69epWzZsmbdF4EZ3NpvEHbRT0DpahB1QT0Mr8WEWTzd4RvPGf7XWfrVKcLH9TOuLCDIGCHuBAIradiwIUOGDEGj0SQTPPXr12fo0KGo1WpTMoWHhwejRo1ixIgR6PV66tSpw8uXLzl06BCenp706tUrxfyffvop9erVY+bMmbRp04Y9e/awbds2i0ulFCpUmJOnT/Eg9B6ubm5ofV0YMmQIv//+O926dWP06NH4+vpy7do1/vzzT5YsWYK7uzv9+vXjf//7H7ly5SJPnjyMGTMmWRP7tNDpdClEjpOTE02aNKFmzZq0a9eO6dOnU7JkSR4+fMiWLVto37491apVo0SJEqxfv542bdogk8kYO3ZsMivk5s2buXXrFvXq1cPHx4etW7ei1+tN7uLAwECOHTvGnTt3cHd3x9fXN8Waz549y/jx4/noo48oW7Ysjo6O7N+/n0WLFvHFF18A0L17d8aMGcOAAQP46quvuHPnDgt/nQ2AMmG+SlWq4erqytdff82wYcM4duwYS5YsSXaujK4nLUaPHk2tWrX49NNPGTBgAG5ubpw4c45NW3fw7Q8/ZXgfzMHV1ZUuH/bm15nT6NWtM1988QXvvvsuQ4cOpX///ri5uXH58mV27drFnDlzKF26NE2aNGHgwIH8+uuvKJVKPv/8c1xcXDL8nRw3bhytW7emUKFCdOrUCblczrlz57h48SJTpkxhyZIl6HQ6atSogaurK3/++ScuLi4ULlzYomstUaIEbdu2ZcCAAcyfPx8PDw++/PJLChQoQNu2bc2+N4I00Otg//ewfzoggV8Z6LwE8pS2ajqtTs+PO68yZ+8NJAk2nH1I3zpFUCpErmdmEXdQILCShg0bEhcXR/HixcmbN69pf/369YmKijKVTDEyefJkxo4dy7Rp0yhTpgwtWrRgy5YtFClSJNX5a9euTXBwMDNnzqRSpUps376dESNGJIsDM4ehnw1HoVDQodG7NKhUnHt375I/f34OHTqETqejWbNmVKhQgZEjR+Ll5WUSQz/88AN169alTZs2NGnShDp16pgsNukRHR1NUFBQsh+juNm6dSv16tWjT58+lCxZkq5du3L37l3T/Zs5cyY+Pj7UqlWLNm3a0Lx582QxXt7e3qxfv55GjRpRpkwZgoODWblyJeXKlQMMblCFQkHZsmXx8/NLEdsGULBgQQIDA5k4cSI1atSgSpUq/Pzzz0ycOJExY8YA4O7uzj///MOFCxcICgpizJgxjBwzEQCFwjCPp7cPf/75J1u3bqVChQqsXLmSCRMmJDtXRteTFhUrVmTz5s1cu3aNunXrEhQUxNTJk8iT1x+5TJbhfTCXPgMGc/vGNdavW0vFihXZv39/snOOGzeO/Pnzm8YvXbqUvHnzUq9ePdq3b8+AAQPw8PDI8HeyefPmbN68mZ07d1K9enXeffddfvrpJwoXLgwYnuvvv/9O7dq1qVixIv/++y///PMPuXLlsvhaFy9eTNWqVWndujU1a9ZEkiS2bt2awhUrsJDIR7C0rUHcIUHQRzBgj9XCLlqlZcDSk/yyxyDsKgRG8VP3AkLY2QiZlMMCDe7fv09AQAC3b99O0zUkeD1ERUVx7do1ypQpY3FAfE5lwIABXLlyhYMHD5p9THismtCwWNN2gI8rPm6OKcYZ3bKenp5mWehyGjuOnqdFzUr8e/AofoGlcHdyoKhfyoQUW5Das3gRreJBRBxeLkoK57JN6MLDiDieR6vw83Ain5eLxccb30///fdfGjdubJM1vWnExsYSEhJCyZIlc26x5Bu7Yf1AiH0OSjdoMwsqfpDq0LCwMObtvZFuQsXNZ9FsP3UDlVaPp5cXvv47+O/Zz7xb8F0O9jmIgzx1p+KdO3coUqQIoaGhFCxY0AYXZr8It6xA8AYzY8YMmjZtipubG9u2beOPP/5g3rx5Fs3x6ve3zPaXzYlIkmRq3eaQYFl4/e3HDP/asoOJIiF4XWdmf9k9e/YQHR1NhQoVePToEaNHjyYwMNCiGnuCtwid1pAJe3AmIEHeCgY3bO60YyfTnU6v5+D155y7/xKdVk/h3HJuO33F5WfncFI48VHFj1DIFDa9hJyKEHcCwRvM8ePHmT59OlFRURQtWpTZs2fTv39/i+ZIrRSKwDKS3jMHuXFf9vSWtWUyoUPCZFozfyk0Gg1ff/01t27dwsPDg1q1arF8+XLh8rRHXj6Adf3g3hHDdrW+0HwqKC238ILBg7DtwiOeRRsyxXN5RnMoejDotZTOXZpVHVdRyb+SrVaf4xHiTiB4g1m9enWm53j1czuHRWLYBL0kUSCgEOdCwymRx4PrT6PQvebi+pJJ3NlO3RmtkOaKu+bNm9O8eXObnV/whnJtJ/z9McSFgaMHvD8bypuXhZ0aIY8i2XPlKVq9hJODDLnLRW6FHQcHLf2C+vFzi5/fmioJbwtC3AkEdo6EcMtmFmOrMYVMhjHeW/faLXeGf23ZojPRcifaQAkAnQZ2T4LDhsxw8lWCToshl7XlSSQOXHvOmdAIAHzcJB5rtxEXF46TwpGZ7/9O/9qWeSIE5iHEnUBg5wi3bOYxuUTlMlORVUP3Bum1FV01Wu6yM+ZOYMdE3IO1feF+Qi3Ndz6GZpPBwboi65IksffqUy48iATA1/MFt2J2gkwiv0d+WgU0pUOZahnMIrAWIe4EAjvnVUudsNxZjlEQy2UyU4cKMFjv5LwecZe4BtvNabTc6SQJvSTZ1OUreIu4sgU2fALxEeDkBW3nQNn3rZ9Pktgd8pRLjwzCzsHlMrdiz4IMagXUolGRRsRHRdlk6YLUEeJOILBzjFpOhgwJyayeqILk6JIkM8gSBJ4uwXLHa0ruM8Xc2VBMKuQy0++FVifh6CDEXY5Cq4Zd4+DYr4bt/FWg82LwCbR6Sp1eYs/VZ9yKkgESKsVxIjQ3cVO60b50O4r5WpdpK7AMIe4EAjvHKOYUchlavSQsd1ZgjLkzumDlchk6nfRa4+6yIuZOJpOhUMjQ6iR0ej2irn0OIuw2rO0DD88YtmsOhcbjwSFlDUxz0er0jPn7AtefRqNwcSdGfgiN7B5FfYrSvnR73B2zpi6kICVC3AkEdo7R4mMQdyLmzhqSumXBcC81utdb606fBdmyYHDNanXmZ8wK7IDLG2HjUFBFgrM3tA+GUu9lakqNTs+wlWfYdvExoCdGfhCd/AGNizSmdqHayF5T+ILAgPiaJhDYITKZjA0bNgCYcmWNwfO2tNwtWbIEb29v0/aECROoXLmyabt37960a9fOZuczh8DAQGbNmmXTOY33TJHw+WQUWPrXKIikLIi5A8tr3QneYjTxsGUUrO5pEHYBNWDQf5kWdiqtjk+Wn2LbxcdIaIiVH8HNJYo+QX2oU6iOEHbZgBB3AkEmOHLkCAqFglatWll8bFaIkNTQJ7HcQaIgefz4MZ9++ilFixbFycmJwoUL07VrV3bv3m31uUaNGpWp4y3hVWFp5MSJEwwcONCm5zK5ZZNY7gBeZ5KpPguyZQEcEtqbaUXGrH3z4iYsbAonfjds1x4OvbeAd0Cmpo3X6Oiz5Ai7Lj9FQs1zxxkU9fPk42qDKOiZubkF1iPcsgJBJli4cCGffvopCxcu5OHDh8marL8pJI25A4OL8c6dO9SuXRtvb29++OEHKlSogEqlYtOmTXz66adcuXLFqnO5u7vj7p65uBq1Wo2jo/VxP35+fpk6f2ropOQxd8aM2dfpls0yy53CeC2i1p3dcmEt/DMc1FHgmgvaz4cSTTM9bZxaR8f5O7j8QEJPPC9dvmda3d7EhNfC2cE58+sWWI2w3AkEVhIdHc1ff/3F4MGDadWqFUuWLEkx5p9//qF69eo4OzuTO3du2rdvD0CDBg24e/cuI0aMQCaTmawxr7o1AWbNmkVgYKBp+8SJEzRt2pTcuXPj5eVF/fr1OX36dJrrfNUtK0kSn3zyCTKZjOPHj9OxY0dKlixJuXLlGDJkCIcPHzYdO3PmTCpUqICbmxsBAQF88sknREdHp3mu1NYPMHHiRPz8/PD09GTQoEGo1WrTaw0aNGDo0KEMHz6c3LlzmzogpHfuffv20adPH16+fGm6fxMmTABSWkTv3btH27ZtcXd3x9PTkw8++IAnT56kWPOyZcsIDAzEy8uLrl27EpWkVMOrMXfybGhBpidrLHcK4Za1XzRx8M9nhjZi6igoVMvghrWBsIuIjaP+T6sShF0cbnmWcHjwYnpV7gXCDZvtCHEneKOQJIlYtTZbfixty7V69WpKly5NqVKl+PDDD1m0aFGyObZs2UL79u1p2bIlZ86cYffu3bzzzjsArF+/noIFCzJp0iQePXrEo0ePzD5vVFQUvXr14r///uPo0aOUKFGCli1bJhMjSUmMFzO84YaFhbF9+3aGDBmCm1vKlj9JXZ1yuZzZs2dz6dIl/vjjD/bs2cPo0aPNXivA7t27CQkJYd++faxcuZL169czceLEZGP++OMPHB0dOXToEMHBwRmeu1atWsyaNQtPT0/T/Rs1alTKa9fradu2LWFhYezfv59du3Zx69YtunTpkmzczZs32bBhA5s3b2bz5s3s37+f7777Lsk8RsudYdvklrUHy51R3Am3rH3x7Br83hhOLQFkUO9/0Osf8My8d+HSk5tUn76Ep+He6ImlVsXTnP50NeXylMv03ALbINyygjeKOI2OsuN2ZMu5L09qjquj+X8SCxcu5MMPPwSgRYsWvHz5kv3799OgQQMAvv32W7p27ZpMyFSqZGiM7evri0KhwMPDA39/f4vW2ahRo2Tbv/32G97e3uzfv5/WrVunGP+qW/bu7VtIkkTp0qUzPNfw4cNN/w8MDGTKlCkMGjSIefPmmb1eR0dHFi1ahKurK+XKlWPSpEn873//Y/LkycgT1FKJEiWYPn262ed2dHTEy8sLmUyW7v3bvXs3Fy5c4Pbt2wQEGOJ/li5dSrly5Thx4gTVq1cHDCJwyZIleHh4APDRRx+xe/duvv32W8Prr2SqKrIhoSLrYu6E5c7uOLcKNo8ETQy4+UGH36BYo4yPM4MV59cw+q9QHHSlkIjhs/cUfF5/qk3mFtgOYbkTCKzg6tWrHD9+nG7dugHg4OBAly5dWLhwoWnM2bNnady4sc3P/eTJEwYMGECJEiXw8vLC09OT6Oho7t27l+p46ZWECp1kfmzVv//+S+PGjSlQoAAeHh589NFHvHjxgtjYWLPnqFSpEq6urqbtmjVrEh0dTWhoqGlf1apVs+TcISEhBAQEmIQdQNmyZfH29iYkJMS0LzAw0CTsAPLly8fTp09N20bdozC5ZRM7O7wusqJDBYBDQrNca2PuYlRabjyN5tHLOOI1OlsuTWAp6hjYMAT+/tgg7ALrGtywNhB2sZpYBm76mGF/HcJBVwqZPI7fe1fg8/odbbBwga0RljvBG4WLUsHlSc2z7dzmsnDhQrRabbIECkmScHJyYs6cOXh5eeHi4mLxGuRyeQr3sEajSbbdq1cvXrx4wc8//0zhwoVxcnKiZs2ayeLYkvKq5a5wYDFkMlmGSRN37tyhdevWDB48mG+//RZfX1/+++8/+vXrh1qtTibYMsur7uHXeW4ApVKZbFsmk6FPInay2y0rSVKW9JaFzMfcPY6MN4U2PItS4erogI+rEm9XJQq5sB+8Np6GwJre8OwKyORQ/0uoNwrkmW+hcvHpRbqu7cq9R0Xx1X2MDIlFverQsJRlXgfB60OIO8EbhUwms8g1mh1otVqWLl3Kjz/+SLNmzZK91q5dO1auXMmgQYOoWLEiu3fvpk+fPqnO4+joiE6X3NLh5+fH48ePkSTJ9CF+9uzZZGMOHTrEvHnzaNmyJQChoaE8f/48zfUaA/GNH+JePj40a9aMuXPnMmzYsBTCKiIiAl9fX06dOoVer+fHH380uU9Xr16d3q1JlXPnzhEXF2cSu0ePHsXd3T2ZNe1VzDl3avfvVcqUKUNoaCihoaGm812+fJmIiAjKli1r9jXo0nLLvibLXdLTZEURYzAIVUv7y6o0OmJUWmSAh7OSqPjEGNZHL+PxclHi4+qIm5PC5qJUkIAkwZk/Yev/QBsH7v7QcQEUqWuDqSV+O/Ubw3cMB1Up8mj6AzCmVVkh7N5wxNcqgcBCNm/eTHh4OP369aN8+fLJfjp27GhyzY4fP56VK1cyfvx4QkJCuHDhAt9//71pnsDAQA4cOMCDBw9M4qxBgwY8e/aM6dOnc/PmTebOncu2bduSnb9EiRIsW7aMkJAQjh07Ro8ePdK1EiYNxDd+wM6aPQedTsc777zDunXruH79OiEhIcyfP5/atWsDULx4cTQaDb/88gu3bt1i2bJlpmQHS1Cr1fTr14/Lly+zdetWxo8fz9ChQ02iLTXMOXdgYCDR0dHs3r2b58+fp+qubdKkCRUqVKBHjx6cPn2a48eP07NnT+rXr0+1atXMvoZXY+5MbtnXVD0kqYi0tUYy9pcF0FmYVBEWa7AWezgrCcztRul8HuTzcsbJQYFekgiPVXPreTRXn0TxJDIetVaUW7EpqmiDC3bTUIOwK9bI4Ia1gbCLiI/gg7UfMGjLILRqL/LrxiJDQYcqBehXp4gNFi/ISoS4EwgsZOHChTRp0gQvL68Ur3Xs2JGTJ09y/vx5GjRowJo1a9i0aROVK1emUaNGHD9+3DR20qRJ3Llzh2LFiplqs5UpU4Z58+Yxd+5cKlWqxPHjx1NkgS5cuJDw8HCqVKnCRx99xLBhw8iTJ0+a600UdzJTvFaRIkU4ffo0DRs25PPPP6d8+fI0b96c/fv3M3fuXMAQKzdz5ky+//57ypcvz/Lly5k2bZrF96tx48aUKFGCevXq0aVLF95//31T2ZK0MOfctWrVYtCgQXTp0gU/P78UCRlgELMbN27Ex8eHevXq0aRJE4oWLcpff/1l0TUYPbTZVefO1FcWmc0tdzKZLIlr1nzxpZckwmMMIQM+boa6hEqFHD8PZ0rmdaeYnzu+bo4oZDLUWj1PIuO58jiS289jiIhVix7HmeXxRfitPpz/C2QKaDwOeqwD98zXeTwSeoTKwZVZe3ktSpk7FR3notM5U6mgF1PbVxBW2LcAmWRp/Ye3nPv37xMQEMDt27eT1Q4TvH6ioqK4du0aZcqUsXkMlSCRSw9eopMkSuX14NbzGDQ6PSXyuOPyivtbr9cTGRmJp6dnula1nMjFBy/RSxKl/T1wdFCg1uq48jgKuUxG+QIpRX5mefVZqDQ6rj7JuvNdexJFvEZHkdxueDgrMz4AeBmn5u6LWBwUckr7e6QpOnV6icg4DWGxamJUWtN+hVxGHg9n/DycbHINWUVsbCwhISGULFkyWdJNtiFJcGoxbPsSdCrwyA+dFkHhmpmeWi/p+f6/7xm7dyw6SUcR72JUcwnm+C0Vfh5O/DO0Dv5e6RcnDgsLY97eG7h5eqc7LiYygk8aFsfX19fs9d25c4ciRYoQGhpKwYIFzT4uJ/JmBzcJBIJMY7TFGIr9JuzLUV/pMockSSndskli7iyNU7MG4zPMqvM4WJEgEma02rkq012XQi7Dx80RHzdHVBod4bEawmPVaHR6HkfGk9vdUViCzCU+0lCU+NJ6w3aJZtAuGNxyZXrqR1GP+Ojvj9h929A+sHuF7lRy+5J5e+/hqJAT/GHVDIWd4M1BiDuBwI5JnmWZXJQIzCPpvXq1tywYMmnliqwVJ5JJXGbN/Mbr0ZgZc6fW6omKN4g7X1fzW8U5KRX4eynI4+nEpQeRSJKERifh6CDEXYY8PAtr+0DYLZA7QOPxUHNoYgp3Jth+Yzs9/+7Js9hnuCpdmdtyLvkcmvPxn4bON1Pal6dqYZ9Mn0fw+hDiTiCwY5J+VCcVd0LbmY8xDE1GouVTJjPEvuklCZ1ewiHz1SbSX4Mx5i6rLHcW1roLT0ikcHdywMmCEkJG5DIZSgcZaq2ERqfH0UGEAaSJJMHx32HnGNCpwSvA4IYNeCfTU6t1asbsHsOMIzMAqJi3In91+guZtgAd5h0CoHetQD6olnZmu+DNRIg7gcCOSRpSKycxoUJY7szHVAZFnlxcKeQy9DrptRQyzmrLnSVdKiRJIizGIO583cy32r2Ko0KOWqtHrdXj9maH3WUfcRGGTNiQfwzbpVpC27ngan6cWlrcDLtJt3XdOPHwBABDqw/lh2Y/EKeS03buIWLUOmoVy8WYVmUyfS7B60eIO4HAjkmqO5K7ZbNpQW8hr8bbGTHdy9dwM7PccmdBf9kolRaNTo9CLsPTzOSL1HBMsBaqX1c9mbeN+6dgbW+IuAdyJTSbDDUG2aQWzsoLK/l488dEqaPwcfZhUdtFtCvdDq1Oz9CVx7kXFkuArwtzu1dBqRBW1bcRIe4EAjsmqShInlAh1J25mMqgvPKhmtjOLevX8Losd+YkVIQnWO18XB1NpWGsweiKFbXvXkGS4Og82DUe9BrwLgydF0OBlC36LCVGHcOn2z5l8dnFANQtVJflHZYT4GVwu07deoVDN17g6qjg957VTCVuBG8fQtwJBHaMlBB1Z/wIFgkVlqNP4pZNyutsQaY3JcVkjbpTJFhnMnLLanR6IuMM5Uwy45KFJOJOWO4SiQ2DjUPg6lbDdpn34f1fwMU701Ofe3yOruu6cuX5FeQyOd/U/Yax9cfiIDfIgDUnQ1l06DYAMz+oRGl/z0yfU5B9CHEnENgxSbtTGP4VCRWWYhRWihRu2YTXX6NbNutj7tIXWuGxaiQkXB0dcLYikSIpRnefRljuDIQehzV9IPI+KByh+VSo3j/TblhJkph7Yi6jdo5CpVOR3yM/yzssp0FgA9OYM/fCGfP3RQA+a1yCFuXzZeqcguxHiDuBwI55tdm80fokLHfmk1bMXaJb9nUmVGR9nbu06vZJSTpSZNZqB4mWO41O/1pqBb6x6PVweDbsngSSDnyLQuclkK9SpqcOiwuj78a+bLy6EYDWJVuzuO1icrvmNo15EhnPx8tOodbpaVY2L581LpHp8wqyHxEpKRC8YUyYMIG8efMik8nYsGFDpuZKjLkz/mtMAsjUtOmyb98+ZDIZERERACxZsgRvb2/T6xMmTKBy5cpZt4BUaNCgAcOHD7fqWF1GMXevNaEia+Y39Jc1kNb1xKh1qLQ65DIZXi7WJ1IYcZAbyslIGARejiTmBaz4AP4dbxB25TvCwP2ZEnZR8RoO33zOnAN7qDyrN7suReClb0Gfkot5v8AsVh0N58edV5m6NYTxGy/Sc+FxnkapKJnXnZldKmcqjlLw5iAsdwKBFfTu3Zs//vgDAKVSSaFChejZsydff/01Dg7W/1mFhIQwceJE/v77b9599118fDJXOFSS4NeZ37Fv51ZCLp7PsBRKZGQk06dPZ/369dy5cwdvb2/Kly/PJ598Qvv27a2K+erSpQstW7bMzGWYzb59+2jYsCHh4eHJBOX69etRKq0TJGnF3L3O+MWsttwZ+svK0er1aHUSqXlcjeVPvF2VyYo4Z+acSoUclVaHRqvHKauLBb5p3D0Ma/tB1ENwcIb3vocqvTKl4C/cf0mvxcdNz0rOxxg7ze45B3vOXUn1OC8XJb/3rIa7k5AE9oJ4kgKBlbRo0YLFixejUqnYunUrQ4YMQalU8tVXX1k8l06nQyaTcfPmTQDatm1rk+B5ieTCIz1BEhERQfPmzYmOjmbKlClUr14dBwcH9u/fz+jRo2nUqFEywWQuLi4uuLi4WLV+I2q1GkdH612BlvSvfJUM3bJ2YLkDgyVNqzfG3SUXWlqdnpdxtnPJGnF0MIi7HJVUodfDfz/C3qkg6SFXCYMb1r98pqY9fPM5/f84Qaxaj5YwdLIw/Ny9KONXAjdHR5wc5DgrFTg5yA0/SgXOCf+2qpCPwrncbHN9gjcC4ZYVCKzEyckJf39/ChcuzODBg2nSpAmbNm0CQKVSMWrUKAoUKICbmxs1atRg3759pmONrspNmzZRtmxZnJyc6Nu3L23atAFALpcnE3cLFiygTJkyODs7U7p0aebNm5dsLffv36dbt274+vri5uZGtWrVOHbsGH8uXUrwT99z5dIFZDIZfh7ObFy9ItWEijFjxhAaGsqRI0fo1asXZcuWpWTJkgwYMICzZ8/i7u4OwLJly6hWrRoeHh74+/vTvXt3nj59muZ9etUta2T+/PkEBATg6urKBx98wMuXL02v9e7dm3bt2vHtt9+SP39+SpUqleG579y5Q8OGDQHw8fFBJpPRu3dvIKVbNjw8nJ49e+Lj44Orqyvvvfce169fT7HmHTt2UK96EO+WKkiPTm159OiRaYwxweJ1ZstmZVyaQpH29UTEaZAkCWelApdMJlIkxVTrTptDYkCjn8KfHWDPFIOwq9gVBu7LtLDbfvERPRceJVatJ15+jkjPz5ne1ZfzY3rz18DaLOpdnV8/rMpPXSrzXceKTGxbnq9blmFks1IMaVicwNxC2NkbwnIneLOQJNDEZs+5la6ZMo24uLjw4sULAIYOHcrly5dZtWoV+fPn5++//6ZFixZcuHCBEiUMAcuxsbF8//33LFiwgFy5cpEvXz4aNGhAnz59komI5cuXM27cOObMmUNQUBBnzpxhwIABuLm50atXL6Kjo6lfvz4FChRg06ZN+Pv7c/r0afR6Pe06duLoqbMcObCbA3v38DJWzUudMoXlTq/X89dff9GpUyfy58+f4tqMwg5Ao9EwefJkSpUqxdOnTxk5ciS9e/dm69atZt+rGzdusHr1av755x8iIyPp168fn3zyCcuXLzeN2b17N56enuzatcuscwcEBLBu3To6duzI1atX8fT0TNNi2Lt3b65fv86mTZvw9PTkiy++oGXLlly+fNnkvo2NjWXGjBn89OsCYtQ6xo0YxKhRo0xrNMYmvY6C0NJrstxBynIor3aksGU5FmNP2Rxhubu1H9YPgOgn4OACrWZA5R6Zfqh/Hr3FNxsuAzJi5IcIKLSbvzofokQukRiRkxHiTvBmoYmFqSnFxWvh64fgaPk3WEmS2L17Nzt27ODTTz/l3r17LF68mHv37pmE0qhRo9i+fTuLFy9m6tSpgEGozJs3j0qVEoOnjRYuf39/077x48fz448/0qFDBwCKFCnC5cuXmT9/Pr169WLFihU8e/aMEydOmNyPxYsXBwylK1zd3HBQOODv749LnAbVi5gUguT58+eEh4dTsmTJDK+3b9++pv8XLVqU2bNnU716daKjo5OJwPSIj49n6dKlFChQAIBffvmFVq1a8eOPP5qu3c3NjQULFiRzx2Z0buP158mTJ00XslHUHTp0iFq1agEGAR0QEMCGDRvo3LkzYHg+wcHBOHj78zJOQ/+PB/Pj91NN89ib5c4hIajw1S4VcRod8RpDIoW3DRIpkpJoubNjcafXwf7psP97QAK/0tD5D8hTOlPTSpLE5G3HWHTgBSAjSrGDnnUd+K7JAZwcRD+3nI4QdwKBlWzevBl3d3c0Gg16vZ7u3bszYcIE9u3bh06nSyGUVCoVuXLlMm07OjpSsWLFdM8RExPDzZs36devHwMGDDDt12q1eHl5AXD27FmCgoJSjSuTpFdj7gz/vmq5e3Vcepw6dYoJEyZw7tw5wsPD0Sek3t67d4+yZcuaNUehQoVMwg6gZs2a6PV6rl69ahJ3FSpUSBFnZ4tzh4SE4ODgQI0aNUz7cuXKRalSpQgJCTHtc3V1pVixYtx6Fg2Af758ydzPitdYVua1WO4Uqde6M1rtvFyUONi4FZXS3gsZRz2Gdf3hzkHDdtCH8N4P4OiaqWn1eokP/1jH4asGy7TaeTN/dGtN61KtMrtigZ0gxJ3gzULparCgZde5LaBhw4b8+uuvODo6kj9/flOWbHR0NAqFglOnTqFQJI9PSmrZcnFxydDFFR1tEBa///57MjECmOZOL1nBZFRKUcQ4uSDx8/PD29uba9eupbuemJgYmjdvTvPmzVm+fDl+fn7cu3eP5s2bo1ar0z3WUtzckltRX+e5AZN7NrGAsCzZfZNnQ4eKLI25S+V6dHqJiFhDIkVWtKIyWu60Oj16vWRfZThu7oH1AyHmGSjdoPVPUKlLpqcNi31Ji3nLePq8CAC+efaxecBY8ntkk8dD8EYiEioEbxYymcE1mh0/Fn5wurm5Ubx4cQoVKpSs/ElQUBA6nY6nT59SvHjxZD9J3a3mkDdvXvLnz8+tW7dSzFWkiOHNvWLFipw9e5awsLAUx0uSQaTodTogqeUu+Ti5XE6XLl1Yu3YtDx+mFNfR0dFotVquXLnCixcv+O6776hbty6lS5dON5kiLe7du5fsPEePHkUul5sSJ1LDnHMbLX26hOtNjTJlyqDVajl27Jhp34sXL7h69Wqq1r9EYZV8vyJJ5nFWW+9MnUay8BymmLskbtmXcWr0koSTgwI3R9uXKlHIZab7aDfWO50Wdk+GZR0Mwi5vefh4v02E3eF7J6k6PZinz4sgoaN+xbucGP6dEHZpoNPpGDt2LEWKFMHFxYVixYoxefLkZF/SJEli3Lhx5MuXDxcXF5o0aZIsuQogLCyMHj164Onpibe3N/369TN98TZy/vx56tati7OzMwEBAUyfPv21XGNaCHEnENiYkiVL0qNHD3r27Mn69eu5ffs2x48fZ9q0aWzZssXi+SZOnMi0adOYPXs2165d48KFCyxevJiZM2cC0K1bN/z9/WnXrh2HDh3i1q1brFu3jiNHjiBJEvkLFiL03l3Onj3LixcvUKtUqYqRKVOmUKBAAWrWrMnSpUu5fPky169fZ9GiRQQFBREdHU2hQoVwdHTkl19+4datW2zatInJkydbfE3Ozs706tWLc+fOcfDgQYYNG8YHH3yQrvg159yFCxdGJpOxefNmnj17luINGKBEiRK0bduWAQMG8N9//3Hu3Dk+/PBDChQoQNu2bVOMN7YXe9VqltTKlNUtyLK6tywkiblLci1hpo4Uyiw5t0wmsy/X7MsH8EcbODgDkKBqH+j/L+TOXHKDJEl8d+BnOgbvRYovj4SaUS09+aP7JyjkOaw+oAV8//33/Prrr8yZM4eQkBC+//57pk+fzi+//GIaM336dGbPnk1wcDDHjh3Dzc2N5s2bEx8fbxrTo0cPLl26xK5du9i8eTMHDhxg4MCBptcjIyNp1qwZhQsX5tSpU/zwww9MmDCB33777bVeb1KEuBMIsoDFixfTs2dPPv/8c0qVKkW7du04ceIEhQoVsniu/v37s2DBAhYvXkyFChWoX78+S5YsMVnuHB0d2blzJ3ny5KFly5ZUqFCB7777DoVCgQQ0afk+DRo1pWHDhgTk92fbxnWpZnj6+vqyc+dOevTowZQpUwgKCqJu3bqsXLmSH374AS8vL/z8/FiyZAlr1qyhbNmyfPfdd8yYMcPiaypevDgdOnSgZcuWNGvWjIoVK6Yo7/Iq5py7QIECTJw4kS+//JK8efMydOjQVOdavHgxVatWpXXr1tSsWRNJkti6dWuqhY7TElZymcwk+LK6BVlW95aFlDF3cWodsWotMpkMb1fbu2SNONpLj9lrOyG4Dtw7DI4e0GkRtJkFyszVeHwW84wWSz9g9jYFTvqyKBQqFvWuwqf1Gthk2fbM4cOHadu2La1atSIwMJBOnTrRrFkzjh8/DhhE86xZs/jmm29o27YtFStWZOnSpTx8+NDUHSgkJITt27ezYMECatSoQZ06dfjll19YtWqVyfuwfPly1Go1ixYtoly5cnTt2pVhw4aZvoBnBzLJkkhqO+D+/fsEBARw+/ZtAgMDs3s5OZqoqCiuXbtGmTJlcHXNXICxIHUevYzjWZSK3O5O5Pd2QavTc/lRJAAVCnglEyx6vZ7IyEg8PT2Rv9qOIQdz4cFLJEmitL+nqR+qkZBHkWh0ekrkccfF0XYhzK8+iyuPIlHr9BTzc8cti7oIaHR6QpL8bjx6Gc/zaBVeLsosLXD7MCKO59Eq/DycyOeVOSGUFcTGxhISEkLJkiXx8PBIOUCnMfSFPTzbsJ2vEnRaDLmKZfrce2/vpcea4cjCP0Up5cPNScfqj+tTLr9XpufOKsLCwpi39wZunt7pjouJjOCThsUtKjB+584dU7WApAlZTk5OODmlzBCeOnUqv/32Gzt37qRkyZKcO3eOZs2aMXPmTHr06MGtW7coVqwYZ86cSdYSsX79+lSuXJmff/6ZRYsW8fnnnxMeHm56XavV4uzszJo1a2jfvj09e/YkMjIyWbvIvXv30qhRI8LCwjLdacgaREKFQGDHvJplmdS1qJdAYUfx61mBXpKStP5K+brJcpfFRifj9FlquUsyuUanJzw2sbZdVvJWl0OJCIW1feG+wRLEOwOh2RTIZCkSrV7LZ5t+YPWJ57jqJiJDib+Xgr8GNhCdJCBFbOz48eOZMGFCinFffvklkZGRlC5dGoVCgU6n49tvv6VHjx4APH78GDDENiclb968ptceP35Mnjx5kr3u4OCAr69vsjFGT0rSOYyvCXEnEAhsSqK4kyX8m/iaXpJQINRdeiSNpUstk9OUYZrlCRVZH3Mnk8lwSOgv+yJGjU4v4aiQZ3m/UaM19K0Td1e2wobBEB8BTl7Q9hcomzJm0xJ0eolVpy4xcesB1HEVMcq4d4p4M6dbVfJ4Omd62fZAapa71Fi9ejXLly9nxYoVlCtXjrNnzzJ8+HDy589Pr169XtdyswUh7gQCO8ZkdUrYliXEiSW1SAnSRp9EHKdWhsSUfZzlCRXG82WtGFck9JcNizZY7Xxs3JEiNd66hAqtGv4dD0cTYkTzV4HOi8En0OopY1Ra1p66z+y9F3kRJQMKI6ElKFBiUusGVCzobYuV2w0eHh54enpmOO5///sfX375JV27dgUMtTPv3r3LtGnT6NWrlymB68mTJ+TLl8903JMnT0xuWn9//xRZ+VqtlrCwMNPx/v7+PHnyJNkY47alFRJsRbYH1sydO5fAwECcnZ2pUaOGKdAxLWbNmkWpUqVwcXEhICCAESNGJMtqEQgEiRg/LpN+QMtNJTyyYUFvGWmVQTHyOix3UgauYVtiTKrQSRIywCcLEymMGN2yOr2ETv+GC7zwO7CoeaKwe3cI9N1htbB7GBHHtK0h1Jy2m/GbLvEiSoaOaFy9D7F6cGk2DGonhF0miI2NTRE/rFAoTMXPixQpgr+/P7t37za9HhkZybFjx6hZsyZgKLAeERHBqVOnTGP27NmDXq831R6tWbMmBw4cQKPRmMbs2rWLUqVKZYtLFrLZcvfXX38xcuRIgoODqVGjBrNmzaJ58+ZcvXo1hY8bYMWKFXz55ZcsWrSIWrVqce3aNXr37o1MJsvWrBSBdRgFh7AgZR2J7rzEfWl1qRCkxHiPFGlYr15HC7KkU2e1FS1p3J27szJFAklWoJDLcJDL0Ool1FoJl6zXkxZhEta398GWIaB6Cc7e0O5XKN3SqjnPhkaw8L/bbL3wyPS7o5E9JNJhIwNqVeD7ZpNQKmzb6i0n0qZNG7799lsKFSpEuXLlOHPmDDNnzjS1MZTJZAwfPpwpU6ZQokQJihQpwtixY8mfPz/t2rUDDHUxW7RowYABAwgODkaj0TB06FC6du1qai/ZvXt3Jk6cSL9+/fjiiy+4ePEiP//8Mz/99FN2XXr2iruZM2cyYMAA+vTpA0BwcDBbtmxh0aJFfPnllynGHz58mNq1a9O9e3cAAgMD6datW7JipIK3BwcHB/R6PbGxsSm6EQhsg6n4bRJNIBOWO7Mx1bhLw2Rm3J+VQjlZV4ysttwlOUFWJ1IkRekgR6vWodbpceHNqtum0ahRaiJx3DEE1C+h4DuGMifeARbPdeDaM37efZ1TdxMzL9WKi0Qo1uPhcZd1HZbSrFgzWy4/R/PLL78wduxYPvnkE54+fUr+/Pn5+OOPGTdunGnM6NGjiYmJYeDAgURERFCnTh22b9+Os3NifOPy5csZOnQojRs3Ri6X07FjR2bPnm163cvLi507dzJkyBCqVq1K7ty5GTduXLJaeK+bbBN3arWaU6dO8dVXX5n2yeVymjRpwpEjR1I9platWvz5558cP36cd955h1u3brF161Y++uijNM+jUqlQqVSm7aioKMDQFDypCVXw+tHr9URFRfHs2TPA0Mszqy0TOQ2NOh5Jq0OtkhMrM3RtkLRqJK2O+LhY5LrEtwBJklCr1cTFxYnnkECcSmu4XzIFsbGxKV7XadRIWjWqOIlYpe0EXtJnodMbnpkMGXFxcTY7R2rotYbrUcjlOEgaYmNfz3ukXKdB0mqJiZWhlN4ci5Veq+LZvet4PfoPB3Ukupqfoq//NSiUYOHnx383XtBv6Sn0kkFEe3lf50LMz2jkt2lSpAmL2pzA393/rf9c0mg0SJIOvT7tLjEAkqSz+HPY0nvj4eHBrFmzmDVrVppjZDIZkyZNYtKkSWmO8fX1ZcWKFemeq2LFihw8eNCi9WUl2Sbunj9/jk6nSzUF+cqVK6ke0717d54/f06dOnWQJAmtVsugQYP4+uuv0zzPtGnTmDhxYor9Bw4c4PLly5m7CIHNiImJEbXVsoAojQyNHmKfSTjK094nSB21DqK1MpRyiejHKV+P10GsVkaEXOJFFmkSnQQv1TJkMtC8yFpzq1Zv+P1wdZC48ixLT5WMOC3E6WREKiRc35A0P4VehaM2CkXcC3JdX83RoiN5Gl8JduyyeK4wFcw4r0AvySjm9ZJL+oncjLuGQq6gZ76etPNsx+kDp7PgKl4/UVFR3Hwgx9ktlZqASYiPiWJX/M3UawemwfPnzzO7vBzDG/JnZB779u1j6tSpzJs3jxo1anDjxg0+++wzJk+ezNixY1M95quvvmLkyJGm7QcPHlC2bFnq1asnihhnMxqNhl27dvHuu+8il8vRarUi/s7GDF99niuPoxjfugw1ixqKhY7ZcIlT9yIY1bQETUomxrZqtVoOHz5MrVq1kvXKzclsufiYX/bepFbRXIxrXTrF6zsvP2Xm3utUK+zNlLblbHbepM8iNELFiBVn8XZRsmrAOzY7R1pIkvTaLbebLzxmzt6bvFvUlwmty7zWcwNceRzFjstPKZvPg6YlvJAfnoUiZAPodShyBbKnyEjqtuqSageTjFBp9XRfcJwYbSR+XrEc0PRHK8UR6BXIn+3+5J0CWf9MXydhYWHcPngLVw/vdMfFRkXQtG5Ri4sYC8wj297Bc+fOjUKhSDV9OK3U4bFjx/LRRx/Rv39/wJDWbPSVjxkzJlXLz6uVqyMjDRXYlUqlVX+oAtsjnkXW8ThGz4MoHY7OLqZvyCoceBClI0bvkOxbs0ajQavV4u7uLp5HApGapzyI0qGVK1O1MLi5xfAgSkfeSJ1FFoiMSPosdFHwIEoHCkebnuNNIl+uOB5E6bj0JP61XaNOL7Hz0mMW/HfbFANXVPaQWt7B+MfdAGRQ93M0dUYRv32n1e9TEzZf4PyDSOSKOM7ED0Unj+ODch8wv/V8vJ29bXtRbwBKpRKZTIE8g563MpnC4nsq3pfMJ9vEnaOjI1WrVmX37t2mrBS9Xs/u3bvT7AeZVloziIxLgSA1jIVhk2Y9uigNfzPxmvRjYgQQozLcIzen1D+oPJ0Nb6FR8dosW0O8xvAMnZX260MP8DW0HwwNi81yy2G0SsvqE6EsPnyb0DBDDKOjQs5I/zN89HwWbnEqXsq9cfxgAS6lm1ocW5eUtafus/zYPST0PFJMw9Exip9b/Eb/Kv1FXKsgS8lW38vIkSPp1asX1apV45133mHWrFnExMSYsmd79uxJgQIFmDZtGmBIa545cyZBQUEmt+zYsWNp06aNSeQJBIJEVAnizimpuHM0/K3EqYW4y4hYtUG0uaXRN9bD2WBJyFJxpzU8J2el/b7HFfA29JSNUesIj9VkSabug4g4lhy6zarjoUSpDM/Lx1VJn+p5GRA9D5dLq0AGR/Xl+DT+E3Jtd2KRfxx+btZ9TJ4LfcEX604DCl46rKB4PjV/dTpJWb+yGR4rEGSWbBV3Xbp04dmzZ4wbN47Hjx9TuXJltm/fbkqyuHfvXjJL3TfffINMJuObb77hwYMH+Pn5merYCASClJgsd0m+/BhFQpyw3GVIdILlzjVNcWe03GVdhqNKY//izlmpIK+nE08iVYSGxdpU3J0NjWDBwVtsu/jYVFOumJ8b/eoUpUPBSJw39INnVwAZNPgSp8D+SH+e5crjKNrOPcT8HpUtPuf5RzfoMP8oOr0PsfITdK3hzU8tjuOidLHZdQkE6ZHtUdNDhw5N0w27b9++ZNsODg6MHz+e8ePHv4aVCQRvP8aWTk7KlG5ZIe4yxmS5S8MtaxR3MWodOr1k6lhhS4xuWafXUFA4Oyno48qTSBX3w+OoFOCdqblSi6cDqF08F/3rFKV+idzIz6+AxaNAGwfueaHjAihSjyBgw5Ba9FtykqtPoui+8ATdi8gwt1zxqourGbnqKo7aKujlT/mxcwU+CpqQqesRCCwl28WdQCDIOhItdyLmzhoSY+7Sd8sCRMdr8XK1fcB3fA6w3AEE+Lhw6m44oeEp6wlaglanp8eCYxy7HQaAUiGjbeUC9K1dhLL5PUEVDRsHw/lVhgOKNoQOv4F7YuZ4QR9X1g6uydAVZ9h/7RmLr8nJ/d9tBjcokWasXKwmluHbh7P6WCTe2o9ApmFhz5o0LZ0yy1ogyGqEuBMI7JhUEypEzJ3ZxCTEZrk6pi6sHB3kODnIUWn1RMZrskTcGeMm7TmhApInVWSGkEdRHLsdhqODnI/rFeWjdwuTxzOh28Dji7CmN7y4DjI5NBwDdUZCKpUWPJyVLOxVjfEbL7L8eCjTd1znXlg8k9uVR6lIPv7i04t0WduFW4+dyaOdAMB3HYJoWrpwpq5FILAW+363EAhyMJIkmdyyScWdiLkzn4wSKiDrkypMljsHe7fcJYi78Mx14Th112Cxq1UsF583K2UQdpIEJxfDgsYGYeeRH3pvgXqjUhV2RhwUcsa3Lk2HQB1yGaw6EUqvRcd5mdC5Q5Ik5p+cT/Xfq3P1yXPyaEYjQ063dwLoWl0IO0H2IcSdQGCnGIUdpF4KJU6jT3GMIDkx6vTdspC0HErWJFWYYu7s3C1b0MeQbHA/k27ZkwkxdlUL+Rh2xEfCun6weTho46F4Uxj0HxSuZdZ8MpmM+vkkfu0RhKujgsM3X9D+10Ocf/CIzms6M2jLIOI1OorLv0MmuVOhgBfj29iuoLVAYA1C3AkEdorRJQuvxNwluBjjhVs2Q4xu2bQSKiBpxmwWWe5MpVDs++3a6Ja9Hx6HXm993dLTRnFX2AcenYPf6sPFdSBTQNNJ0H01uOWyeN5GpfxYO6gW+bycufUshjZz9rP54mWUciUt8y4mPi4P3q5Kfv2wit3HRwrefOz73UIgyMGo0hJ3wi1rNokxd2a4ZVVZZbnLGQkV+bycUchlqLV6nkWrrJrjYUQcD1/GI5dJVH26FhY0gbBb4BUAfbdD7c/SdcNmRCl/NxpXO4VafgOZ5IG/eho9Cm/j0l1vZDKY3TWIggnuZYEgOxEJFQKBnWK03CkVMuRJSnSImDvzkCSJ2ATrpns6btkst9wZO1TYecydg0JOPi9n7ofHERoWS15jEoQFnL4XjicxzHNfgtPOQ4adpVpC27ngan4P09R4GPWQvpv7suf2HmSOTlR2mUVYeAB7L8cDMKJJSeqV9MvUOQQCWyEsdwKBnaI2dadILgpcRbasWai0erQJ7kHXbHTLGosYO9m5WxaSJlVYF3f36PIhNjt+TR3NIZArofk06Loi08LuVOQpqi2oxp7be3BVurKoXTAnRg3k43pFAWhWNi9DGxbP1DkEAlsiLHcCgZ2SWqYsJIm5E5a7dIlNIn5d03GJGt2ykVmVUGGMubPzIsaQJKkizMKMWUmCo7/S+8pYlHItMa4FcOuxDApUzdR61Do1X/z7BbNuzQKgsn9lVnVcRancpQD4qmUZBtQrSi43R9ErVvBGIcSdQGCnpFbAGETMnbkY4+2clXIcFGkLK6PLNussd8Y6d/btloUkte4ssdzFhsHGIXB1K0pgm646FXsuw80/X6bWciPsBt3WdePkw5MADKk2hBnNZ+DskNxdnNvdKVPnEQiyAiHuBAI7RZVKAWNIHnMnSZKwOKRBjBk17uB1ZsvmBHFnsNyFmmu5Cz0Oa/vCy1D0ckfGq7qz07U1R/P6Z2odKy6s4OPNHxOtjsbXxZeP/T9mYrOJKB1sX6RaIMgK7N/OLxDkUFLrTgGJbllJSp5RK0iOsfVYevF2AJ4JbtnoLK5zZ++lUMCCmDu9Hg79DIvfg5eh4FOENZUXsUzXjGqBuaz+whKtjqbPxj70WN+DaHU09QrX42S/k9TwqmHVfAJBdmH/7xYCQQ7FFHP3iksxaeyWSKpIG3O6U8DryJY1JlTYv+XOWEbk0ct4tLo0vnjEvICVXWDXONBroVwH+PgAO8IM1roqhX2sOvfZx2ep9ls1lpxdglwmZ0L9CezpuYeCngWtmk8gyE6EW1YgsFOMWZavWu4cFHIcFXLUOj1xGh3WfRTaP0bLXXrdKUC0H7MleTyccHSQo9bqefQy3hSDZ+LuYVjbD6IegsIJ3vseqvZGLxnKoEBC8WILkCSJOcfnMGrXKNQ6NQU8CrC8w3LqB9YHQJ+WyBQI3mCEuBMI7JS0smXB4OIzijtB6iQWME5fVHm8tvZj9u9okctlFPR24dbzGELDYxPFnV4P/82EvVNB0kGuEtB5CfiXB+DWs2giYjU4Ocgpm8/T7PO9iH1B30192XR1EwBtSrZhcdvF5HK1vIOFQPAmIcSdQGCnJNa5SykKXBwVRMZrhVs2HYxu2fQKGMNrqHOnzTmWO4CCvq7ceh5jKIdSDIh+Bn8PhJt7DAMqdoFWM8HJ3XSMseVYpYLeqX6ZSY0Ddw/QY30P7kfex1HhyIymMxj6zlCRYCSwC4S4EwjslHTFnVLUusuImAThm17rMUh0y0artej1UrJuILZAlYMSKiCx1l1oeCzcPgDr+kP0E3BwgVYzoHIPeEWAnTL2kw3M2CWr0+uYcmAKkw5MQi/pKZmrJKs6riIoX5DtL+YtR6/XExERYdZYb29v5Jlo7SawLULcCQR2SvpuWVHrLiOMblm3DLJljZY7STIIPGP2rK3ISaVQwJAxK0dPuWu/wpE/QNKDX2mDGzZPmVSPOXk3DICqhdIXd/cj79NjfQ8O3D0AQO/KvfnlvV9wd3RP97icSkREBDM3n8HZzSPdcfExUYxsHYSvb+Y6gQhshxB3AoGdklYRY0gshyLcsmljbkKFs1JhSlCJiretuNPpJTQ6yXSenEBxlyiWKadR+/klw47KH0LL6eDolur48Bg1N5/FAOlnym66uok+G/sQFheGu6M7wa2C6VGxh83Xb284u3ng5umd3csQWIgQdwKBnZJWEWMQXSrMIbEUSsaiysPZgRcx6oSkChebrSGp2zxHuGVv7qHB3gEoFc+JxRnX9j9Dpa7pHnIm1OCSLZrbDV83xxSvx2vjGb1rNL8c/wWAqvmqsrLjSkrkKmH79QsEbwg54N1CIMiZpFXEGETMnTlEm7JlM/4OnFVJFfFJikzbdUKFTgu7J8OyDijjnxOiD6CNajLxZTtneKgp3i4Vq93V51epubCmSdiNfHckh/sdFsJOYPcIy51AYKeYLHeKlKLAWbhlMyQ24d5klC0LSWvd2bYciqlWoUJu80SNN4bIh4badfcOAyBV7UP3E40IlxQ8jIijqF/68XAn76QUd5Ik8ce5Pxi6dSgxmhhyu+bmj3Z/0LJEy6y7DoHgDUKIO4HATjHHchenEQVa08JU5y6DhArIQsudJu2MZ7vg+i74+2OIfQGOHtBmFrIKnchz4wDhT6IIDU9f3Gl0es7djwASxV2kKpJPtnzC8gvLAWgY2JA/O/xJfo/8WX45AsGbghB3AoGdotYltK0SMXdWEWNm+zFIFHeRNhZ3Ruur3bUe02lgz2RDf1gA/4qGbNhcxQAI8HXh6pMoQsPS7zEb8iiSeI0eT2cHivm5c/LhSbqu7crN8JsoZAomNpjIl3W+RCG3s/snEGSAEHcCgZ2SruXOUcTcZUSsmdmykHVu2cQyKHZkuYsIhXX9IPSYYbv6AGg2BZTOpiHGHrOh4emLO2O8XVAhH2Yd+4kv//0SjV5DIa9CrOiwgtqFamfNNQgEbzhC3AkEdkp6RYxNde5EzF2aGC13GbUfg6xzyyYWMLYTy9OVrbBhMMRHgJMXtP0FyrZNMcxYyPh+WFy60xnF3eWX/7B05xQAOpTpwII2C/BxEV2TBTkXIe4EAjslvSLGwi2bMebWuQNhucsQrRr+nQBH5xq281eBTovAt0iqw409Ze9nYLk7dPMRABfCN+Lk6MSsFrP4uOrHooWYIMcjxJ1AYKekV8TYaI0S4i51JElKjLkzI6HCM4sTKt7qMijhd2BtX3hwyrD97ifQZCI4pKxJZyTA5JZN3XKn1WsZte1bwmOqIaGjiJ+c1R+coELeCrZevUDwViLEnUBgp5hTxDheuGVTJV6jRzI0hrAoocL2btm3vPXY5U2wcSioXoKzF7T7FUq3yvCwgr4Gt2xYjJoYlTaZ9fRuxF26r+/O2dsK/KiGp1sUxwYdxlXpmmWXIRC8bbzltn6BQJAW6Yk7Z2G5SxdjAWNIFMLpkXVu2be0FIpWBVv/B6s/Mgi7gtVh0H9mCTsAT2clXi6Ge5o0qWLd5XVUnl+Zw6GH8ZBVAqBjpcpC2AkEr/CWvWMIBAJzSbe3rIi5S5ekrcfMKR5sLHRse7fsW2i5e3ETFjaF478Ztmt/Bn22gXchi6YJ8E1MqojTxDFo8yA6relERHwENQrUoJJvOyD9frICQU5FiDuBwE5Rp1MjzUVky6aLMZnC1YxkCshCt6zpGb4lb9UX18P8+vDoHLj4Qvc10HQSKJQWT2WMuzsReot3FrzD/FPzAfii9hfs6LGXG09VQOptxwSCnI6IuRMI7BRTtmxqljtHwz5huUudmCSWO3PIuvZjb0kpFE0cbP8KTi02bBeqCR0XglcBq6c0lkP56b+lPHO4SB63PCxrv4xmxZpx5OYLdHoJf09n8ns5ZzCTQJDzEOJOILBT0itiLOrcpY+x9Zg5ZVAgMVs2WqVFkiSbleIwlUJ5k7Nln1+HNb3hyUVABnVHQoOvQWH9x8vL+JfsvLsKqAG6XDQr1Yyl7ZaS1z0vAKfvJfSTDfQRZU8EglQQ4k4gsFPSK2IsYu7SJzZB9JqTKQuJlju9BDFqnSkGL7OYSqG8qW7Z86vhn+GgiQHX3NDhNyjeOFNTHr1/lG7ruvH4RW7yUoMA98ps6zEJuSzxHhiLF1ctJFyyAkFqCHEnENgp6RYxFu3H0sWYLetqRo07MIgvB7kMrV4iKl5jM3Gn0r6hCRXqWNg2Gs4sM2wH1oWOC8DD3+op9ZKeHw79wDd7v0Gr11LYMy88B7XKAxmJ1jm9XkoUdyLeTiBIlTf066BAIMgs5mTLanQSmgQRKEgk1uiWNdNyJ5PJsiSp4o203D29Ar83ShB2Mqj/JfTcmClh9zj6MS3+bMGXu79Eq9fSpVwXjn28FYAolZaXcYmxjLeeR/MyToOzUk7Z/J6ZvRqBwC4RljuBwE4xWn3Si7kDg/VOmYoAzMnEGN2yZlruwOCaDY/V2DSpwmhZdXpTYu7OLIctn4M2DtzzQoffoWj9TE258+ZOPvr7I57GPMXFwYVf3vuFvkF9kclk+Hk48SxKRWhYHN6uho4WRqtdxYLe4vdWIEgDIe4EAjtEr5fQ6AwtFlITd04OcmQykCRD3J0xZkxgwJhQ4Wqm5Q4Sy6FE2tByZyyFku2WO1U0bB0F51Yatos2MAg79zxWT6nRafhmzzdMPzwdgAp5KrCq0yrK+pU1jSno42IQd+GxVCjoBSSKu2rCJSsQpIkQdwKBHaJO4mpNLaFCJpPholQQq9YRrxZu2VeJtcpyZ3u3bKK4y0bL3ZNLhmzY59dAJoeGX0Odz0FuveC8FX6Lbuu6cfzBcQAGVxvMj81+xEXpkmxcgI8rZ+5FcD9JlwoRbycQZIwQdwKBHZJU3KVmuQNM4k5kzKbE0lIokDW17rLVLStJcPoP2PYFaOPBI5+hdl1g7UxN+9fFvxi4eSCRqki8nb1Z+P5COpTpkOpYY5eK0LA4AMJj1Nx8FgNAkMiUFdgJcXFxSJKEq6uhcPfdu3f5+++/KVu2LM2aNbNqTiHuBAI7xJhMAaknVECSWndC3KUgsYix5W5Zu0ioiI+EzcPh4jrDdvGm0D4Y3HJbPWWsJpbPtn3GgjMLAKgVUIsVHVZQ2LtwmscYu1QY+8sa69sV9XPD183R6rUIBG8Sbdu2pUOHDgwaNIiIiAhq1KiBUqnk+fPnzJw5k8GDB1s8p4hGFQjskKSZsmkVeTWWQxGFjFNiaj9mZocKMDS7B9ta7rKlFMqjc/BbfYOwkymgyUTovjpTwu7CkwtU+60aC84sQIaMMXXHsL/3/nSFHUBBo7gLM4g7EW8nsEdOnz5N3bp1AVi7di158+bl7t27LF26lNmzZ1s1p7DcCQR2SHrdKYwYy6GIWncpiU2w3FlSry5rLXevQdxJEpxYADu+Bp0aPAtCp0VQqEYmppQIPhnMiB0jUOlU5HPPx58d/qRRkUZmHW90y94PN7itRLydwB6JjY3Fw8MDgJ07d9KhQwfkcjnvvvsud+/etWpOYbkTCOyQ9AoYGxFdKtIm2mi5y25xpzXG3GXxW3X8S1jTy5ARq1NDyfdg0MFMCbvwuHA6ru7IJ1s/QaVT0bJES84NOme2sAPI7+2CXGZILHn0Mp5z9yMAIe4E9kXx4sXZsGEDoaGh7NixwxRn9/TpUzw9ravlKMSdQGCHGBvOpxVvB+As3LJpEmuKubOszh3Y2i37Gix3D05BcF24vBHkSmg+FbqtBFdfq6c8dO8QledX5u8rf6OUK5nZbCb/dPsHPzc/i+ZRKuTk8zJY73Zeeky8Ro+Xi5Kiud2tXptA8KYxbtw4Ro0aRWBgIDVq1KBmzZqAwYoXFBRk1ZzCLSsQ2CFqXYLFJ51AfFdhuUsTY8ydZdmyWVDnLisTKiQJjgXDzrGg14B3Iei0BApWtXpKnV7HtP+mMWHfBHSSjuK+xVnVcRVV81s/ZwEfFx5ExPH32YcAVCnkjVyeehypQPA20qlTJ+rUqcOjR4+oVKmSaX/jxo1p3769VXMKcScQ2CGqdFqPGRH9ZdMmxsL2Y5DUcmcbcaeTQKs3FKJ2tnUplNgw2DgUrm4xbJdpA+/PARdvq6d8GPWQD9d/yN47ewHoUaEHv7b6FQ8nj0wtNcDHleO3wzgXGgFAtUDrLYoCwZvInj17qFWrFv7+yVv4vfPOO1bPKcSdQGCHmJNQYSqFItyyydDpJZM109WKIsbRKtu4ZTVJakvb1C0begLW9oGXoaBwhGbfwjsDII2sanPYcm0LvTf25nnsc9yUbsxtOZeelXqmmaltCcakCiNVRH07gZ3x/vvvo9VqqV69Og0aNKB+/frUrl0bFxeXjA9OAxFzJxDYIZZkywq3bHKS3g9LsmU9bZxQkVTc2SShQq+HQ7NhcQuDsPMpAv12Qo2BVgs7lVbFyB0jab2yNc9jn1PZvzKnBp6iV+VeNhF2kFjrDkAhl1EpwMsm8woEbwrh4eHs3r2b9957j+PHj9O+fXu8vb2pXbs233zzjVVzCnEnENghpmzZdN2yhteEuEtObIJLVi6zTFS5OyW6ZSVJyvQ6jOLO0UGe+RizmBewsivsGgt6LZTrAB8fgPzWBWsDXH9xnVqLavHT0Z8AGPbOMI72O0qp3KUyt9ZXCPBNFHdl83la1O9XIHgbUCqV1K5dm6+//podO3Zw9OhRunXrxvHjx5k2bZpVc4q/EoHADrHIcifcssmIThJvZ4n1yeiWNbp1MytCjOLOObNWu7tHYF0/iHwACid47zuo2idTbtg/z//J4C2DiVZH4+viy+K2i3m/1PuZW2caFPRJdE2JEigCe+TatWvs27ePffv2sX//flQqFXXr1mXGjBk0aNDAqjmFuBMI7BCjuEvP8iTaj6VOrNryTFkwdLNQyGXo9BJR8VqbiTsna+Pt9Ho49BPs+RYkHeQqDp2XgH8Fq9cUrY5m6Nah/HHuDwDqF67Pnx3+pKBnQavnzIi8ns4oFTI0OkmIO4FdUrp0afz8/Pjss8/48ssvqVChQqbDGoS4EwjsEJU5ljtR5y5VjJmyliRTAMhkMtydHHgZpyEqXkNeT+dMrcNkubOmDEr0M/h7INzcY9iu2AVazQQn6+vDnXl0hq7runLtxTXkMjnj649nTN0xKORZ2z1DIZfRuHRezoSGU6e49S3QBII3lWHDhnHgwAEmTZrE5s2badCgAQ0aNKBOnTq4urpmPEEqCHEnENghiZa7tD94RUJF6sSoLS+DYsTD2SDubFHrTqM3fHO3uAzK7YOwrj9EPwYHF2j5AwR9aLUbVpIkfjn+C//b9T/UOjUFPQuyvMNy6hWuZ9V81hD8UVX0eknUtxPYJbNmzQIgIiKCgwcPsn//fsaMGcOlS5cICgri0KFDFs8pxJ1AYIeYlVAhesumSmIBY8stUoZad3E2yZhNtNyZuQ69Dg7MgP3fgaSH3KXggz8gTxmr1/A89jl9N/bln2v/ANC2VFsWvr+QXK65rJ7TWoSwE9g7Op0OjUaDSqUiPj4elUrF1atXrZpLiDuBwA4xxy1raj8mxF0yYjNpuQPbtCCzyC0b9QTW94fbBwzblT+EltPB0c3q8++/s58e63vwIOoBjgpHZjabySfVP7FZiROBQGBg2LBh7Nu3j8uXL+Pj40O9evUYMGAADRo0oEIF62JkhbgTCOwQkS1rPdEqYwFjy98ebVnrzmzL3c29sH4AxDwDpSu0/gkqdbX6vFq9likHpjD5wGT0kp5SuUqxqtMqKvtXtnpOgUCQNo8ePWLgwIE0aNCA8uXL22ROUedOILBDLBF38Umr5QpMde7crXbL2tZyl2bcpE4Le6bAsvYGYZenHAzcnylhdz/yPo3+aMTE/RPRS3r6VO7DqYGnhLATvLU8ePCADz/8kFy5cuHi4kKFChU4efKk6XVJkhg3bhz58uXDxcWFJk2acP369WRzhIWF0aNHDzw9PfH29qZfv35ER0cnG3P+/Hnq1q2Ls7MzAQEBTJ8+3ew1rlmzhqFDh9pM2IEQdwKBXaLWGaxP5vSWFW7Z5MQkWDKtKWXiYUPLndpUCiWVZxj5EJa+Dwd+ACSo0gsG7Aa/klafb+OVjVQKrsTBewfxcPRgeYflLGq7CLdMuHYFguwkPDyc2rVro1Qq2bZtG5cvX+bHH3/ExyexpM706dOZPXs2wcHBHDt2DDc3N5o3b058fLxpTI8ePbh06RK7du1i8+bNHDhwgIEDB5pej4yMpFmzZhQuXJhTp07xww8/MGHCBH777Tez17ps2TJq165N/vz5uXv3LmBItNi4caNV1y7EnUBghwi3rPXEmIoYW2O5s52405qKGL+yjuv/QnAduHsIHN2h40J4fzYoretDGa+N59Otn9Lur3aExYVRLX81Tn98mu4VumfyCgSC7OX7778nICCAxYsX884771CkSBGaNWtGsWLFAIPVbtasWXzzzTe0bduWihUrsnTpUh4+fMiGDRsACAkJYfv27SxYsIAaNWpQp04dfvnlF1atWsXDhw8BWL58OWq1mkWLFlGuXDm6du3KsGHDmDlzplnr/PXXXxk5ciQtW7YkIiICXcKXc29vb1MmraUIcScQ2CEqC4sY26Jdlr1gKoViRcyd0S0baRO3bEIpFKPlTqeBXeNheUeIfWEoRvzxAajQyepzXHl+hXcXvMucE3MA+Lzm5xzqe4jivsUzvX6BIKuIiooiMjLS9KNSqVIdt2nTJqpVq0bnzp3JkycPQUFB/P7776bXb9++zePHj2nSpIlpn5eXFzVq1ODIkSMAHDlyBG9vb6pVq2Ya06RJE+RyOceOHTONqVevHo6OjqYxzZs35+rVq4SHh2d4Pb/88gu///47Y8aMQaFI/DJXrVo1Lly4YOZdSY4QdwKBHWJOhwqXJJYpoxgUQGwmEipsablLllDx8j4saQWHZhl2Vh8A/f6FXMWsmluSJBafWUzV36py7sk5/Fz92Np9KzOazcBR4ZjxBAJBNlK2bFm8vLxMP2n1X7116xa//vorJUqUYMeOHQwePJhhw4bxxx+GDiuPHz8GIG/evMmOy5s3r+m1x48fkydPnmSvOzg44Ovrm2xManMkPUd63L59m6CglH2enZyciImJyfD41Mh2cTd37lwCAwNxdnamRo0aHD9+PN3xERERDBkyhHz58uHk5ETJkiXZunXra1qtQPB2YI5bNmnPUuGaTSSxiPGbkVBRLuqQwQ0begycPKHzH9BqBiit64ARqYqkx/oe9N3Ul1hNLI2LNObcoHO8V+K9TK9ZIHgdXL58mZcvX5p+vvrqq1TH6fV6qlSpwtSpUwkKCmLgwIEMGDCA4ODg17zi9ClSpAhnz55NsX/79u2UKWNdncpsLYXy119/MXLkSIKDg6lRowazZs0ymTJfVcoAarWapk2bkidPHtauXUuBAgW4e/cu3t7er3/xAsEbjKmIcTrizkEhx1EhR63TE6fR4Z7JXqj2ginmLpstd3qdljEOq+lwNeHLa/4g6LQYfItYPeeJByfouq4rt8JvoZApmNxwMqNrj87yFmICgS3x8PDA09Mzw3H58uWjbNmyyfaVKVOGdevWAeDv7w/AkydPyJcvn2nMkydPqFy5smnM06dPk82h1WoJCwszHe/v78+TJ0+SjTFuG8ekx8iRIxkyZAjx8fFIksTx48dZuXIl06ZNY8GCBRkenxrZ+m4+c+ZMBgwYQJ8+fQAIDg5my5YtLFq0iC+//DLF+EWLFhEWFsbhw4dRKg3fkAMDA1/nkgWCtwJTEWNF+h/azspEcSfKXhqITbBiWlPE2GZ17iLuMi5mMiUdbhq23/0EmkwAByerptNLemYemclXu79Cq9dS2KswKzquoFZArcytUyB4g6ldu3aKDg/Xrl2jcOHCgMFi5u/vz+7du01iLjIykmPHjjF48GAAatasSUREBKdOnaJq1aoA7NmzB71eT40aNUxjxowZg0ajMWmTXbt2UapUqWSZuWnRv39/XFxc+Oabb4iNjaV79+7kz5+fn3/+ma5drSttlG3v5mq1mlOnTiUzp8rlcpo0aWIKZHyVTZs2UbNmTYYMGcLGjRvx8/Oje/fufPHFF8mCEJOiUqmSBVtGRUUBoNFo0Ggy7zoRWI/x/ovnYHtUCeVNFOjTvb8ujgoi47VExarQuBmsfDn9eUQnuFQdFZLF98JFYUiCiIq3/v1FdmULDps/paQ+kpeSKycrTaFe454gAVbM+TTmKf3+6ceOWzsA6FC6A8Etg/F29s7xz9occvL7lEajQZJ06PXph21Iks70mWrNMZasxxJGjBhBrVq1mDp1Kh988AHHjx/nt99+M5UokclkDB8+nClTplCiRAmKFCnC2LFjyZ8/P+3atQMMlr4WLVqY3LkajYahQ4fStWtX8ufPD0D37t2ZOHEi/fr144svvuDixYv8/PPP/PTTT2avtUePHvTo0YPY2Fiio6NT9V5aQraJu+fPn6PT6VINQrxy5Uqqx9y6dYs9e/bQo0cPtm7dyo0bN/jkk0/QaDSMHz8+1WOmTZvGxIkTU+w/cOAAly9fzvyFCDLNrl27snsJdsfzMAUg4+yZU6hup50Jq1cbxu098B93E7wcOf15hEcb7snpY4d5ZGGiWoQKwIHIODVbtmzFkk5dcr2Gcg9XUfSZ4f6HyIrTP/5T6r3wJdrKuOJzUeeYdXcW4dpwHGWO9CvQj2ZOzTi857BV8+VkcuLfRVRUFDcfyHF280h3XHxMFLvib+Lh4WHVMeby/Plzs8cCVK9enb///puvvvqKSZMmUaRIEWbNmkWPHj1MY0aPHk1MTAwDBw4kIiKCOnXqsH37dpydE2Naly9fztChQ2ncuDFyuZyOHTsye/Zs0+teXl7s3LmTIUOGULVqVXLnzs24ceOS1cIzF1dXV1xdXS0+7lVkUjbVQHj48CEFChTg8OHD1KxZ07R/9OjR7N+/35RinJSSJUsSHx/P7du3TZa6mTNn8sMPP/Do0aNUz/Oq5e7BgweULVuWa9euCZduNqPRaNi1axdNmzY1mbIFtqHF7EPcfBbDn32rUaOIb5rj2sw9wpXHUSzuVZUahT3F8wDKT/wXlVbPvs/rUsDbstpx0SotQVP2AHBhXOOMW4cZCb+NYn1/5I/PAaCpPpg2Z2twJcqRmZ0r0KZivgwmSI5Gp2HiwYn8cPgHJCTK5C7D8nbLKZ/HdhXwcwo5+X0qLCyM3w7ewtXDO91xsVERDKxbFF9fX6uOMZc7d+5QsmRJQkNDKViwoNnHvYlUqVKF3bt34+PjQ1BQULo9m0+fPm3x/NlmucudOzcKhSLVIMS0AhDz5cuHUqlM5oItU6YMjx8/Rq1WJ6sxY8TJyQknp8Q4lcjISACUSmWO+0N9UxHPwvYYEypcnBzTvbeuCRmhGj2mcTn5eWh1elO8opers8X3wdvBAbkM9BLE6cDD1YzjL66HTcNAHQUuvtA+GIo0Iv6MwY3q5pz+M3yVOxF36L6uO0fuG8JbBlYZyE8tfsJVmXlrQE4mJ/5dKJVKZDIF8gwSbmQyhen+WHOMJeuxF9q2bWvSJm3btk1X3FlDtok7R0dHqlatyu7du02+bb1ez+7duxk6dGiqx9SuXZsVK1ag1+uRyw3xQdeuXSNfvnypCjuBIKdiTp07EC3IXiUmSUkYVyt6y8pkMtydHAxxjPFa8qTncdLEw46v4OQiw3ahmoZuE14FQKNJXufOTNZeXkv/Tf15qXqJl5MXv7f5nc7lOlt8HQKBIGtJGko2YcIEm8+frXXuRo4cye+//84ff/xBSEgIgwcPJiYmxpQ927Nnz2QJF4MHDyYsLIzPPvuMa9eusWXLFqZOncqQIUOy6xIEgjcSs8WdaEGWjNiEGndKhQynV9t+mUlirbt0Mmaf34AFTRKFXZ2R0GuzQdglYBJ3GTxDgDhNHIM2D6Lzms68VL3k3YLvcnbQWSHsBIK3gP79+7Nv3z6bzpmttQ+6dOnCs2fPGDduHI8fP6Zy5cps377dlGRx7949k4UOICAggB07djBixAgqVqxIgQIF+Oyzz/jiiy+y6xIEgjcSc4oYQ/IWZILEGneumaj5l1jrLo3MvvOr4Z/hoIkB19zQYT4Ub5JimNpMy92lp5fosrYLl55dQoaML+t8ycQGE1Eq7MeFJRDYM8+ePaNFixb4+fnRtWtXPvzwQypVqpSpObO9sNXQoUPTdMOmpmRr1qzJ0aNHs3hVAsHbjTlFjCGJ5U6IOwBiVMYad9YX9U2zkLE6FraNhjPLDNuBdaHD7+CZerJERm5ZSZL4/fTvfLb9M+K18fi7+7Os/TKaFE0pFAUCwZvLxo0bCQ8PZ82aNaxYsYKZM2dSunRpevToQffu3a1K/sz29mMCgcC26PUSGp0hCd5RYV7MXbxwywJJWo9Z0Z3CSKotyJ5egd8bJQg7GdT/AnpuTFPYQaK4S821HhEfQZe1Xfh488fEa+NpXqw55wadE8JOIHhL8fHxYeDAgezbt4+7d+/Su3dvli1bRvHixa2aL9stdwKBwLYYrXYgLHeWYrTcuWZK3L1iuTuzHLaOAk0suOWBjgugaP0M59GmYbk7EnqEbuu6cfflXRzkDkxrPI2RNUcil4nv6gLB245Go+HkyZMcO3aMO3fupKgFbC42fTeIi4uz5XQCgcAKLBF3IuYuOcaEClu4ZeNiIuHvQbDxE4OwK9oABh8yS9hpdHr0GEojOCsNz1Av6fnuv++ou7gud1/epahPUQ71PcSoWqOEsBMI3nL27t3LgAEDyJs3L71798bT05PNmzdz//59q+azieVOpVIxZ84cfvjhBx4/fmyLKQUCgZWoNEnEnZlu2Ti1Pt1xOQVTzF0m3bKlZPfoevYriL8LMjk0+BrqjoQMan8ZiU/yDJ2VCh5HP+ajvz/i31v/AtC1fFfmt56Pp1PGzdMFAsGbTYECBQgLC6NFixb89ttvtGnTJll9Xmsw+x1MpVIxYcIEdu3ahaOjI6NHj6Zdu3YsXryYMWPGoFAoGDFiRKYWIxAIMk/SZIqMCmMa3bLxwnIHJGbLWm25kyTeCfuHzxy/xzleAx75DG7YwDoWTaPSJj6PvXd20mtDL57FPsNV6cov7/1Cn8p9bF70VCAQZA8TJkygc+fOeHt722xOs8XduHHjmD9/Pk2aNOHw4cN07tyZPn36cPToUWbOnEnnzp2TdY4QCATZg6nGXQZWOxAxd69iTKiwKuZOFQX/DKfhtbUgg4su1Sk/aBW45bZ4KqPlTi7X0XJFawAq5q3Iqo6rKONXxvK1CQSCN5YBAwYAcOPGDW7evEm9evVwcXFBkiSrv8SZ/Q62Zs0ali5dyvvvv8/FixepWLEiWq2Wc+fOiW+QAsEbhLk17gCcHUUR46TEJtwHd0vF3aPzsKY3hN1EL1MwXd2ZC/l7sdwKYQdw48VdADT6WACGVB/CjGYzcHZwTu8wgUDwFvLixQs++OAD9u7di0wm4/r16xQtWpR+/frh4+PDjz/+aPGcZkfh3r9/n6pVqwJQvnx5nJycGDFihBB2AsEbhiXiTljukhNtKmJsphdCkuDEAkO3ibCb4FmQ042XE6x7n0iVdXGMKy+s5IN1PQCQybSs/2A9c1rOEcJOILBTRowYgVKp5N69e7i6JvaA7tKlC9u3b7dqTrO/nup0umT9Wx0cHHB3d7fqpAKBIOtQ6wxCzRJxJ2LuDMSaYu7MeGuMfwmbhsHlDYbtku9Bu3lITwGOpN2hIg1i1DEM2zaMRWcX4aQrjRdQwDMP7cs0s2gegUDwdrFz50527NhBwYIFk+0vUaIEd+/etWpOs8WdJEn07t3blMERHx/PoEGDcHNzSzZu/fr1Vi1EIBDYBpXRcmdOzJ2jYYyw3BmIUZuZLfvgNKztA+F3QO4ATSZCzSEgk+HhHAlk0Fv2Fc49PkfXdV258vwKMmT0KN+H3afB3ckx44MFAsFbTUxMTDKLnZGwsDCrs2bNFne9evVKtv3hhx9adUKBQJC1WBRzpxQxd0kxZcs6peGWlSQ4Nh92fgN6DXgVgs6LoWA105DEDhUZiztJkph3Yh6f7/wclU5Ffo/8LO+wHFV0SXafPpNhX1mBQPD2U7duXZYuXcrkyZMBkMlk6PV6pk+fTsOGDa2a02xxt3jxYqtOIBAIXi8qEXNnNUbLnWtqbtm4cNg4FK5sNmyXbg1t54CLT7JhxiLGap0elVaHk0PqAi0sLox+m/qx4coGAFqXbM3itovJ7ZqbTWdCgdRbjwkEAvti+vTpNG7cmJMnT6JWqxk9ejSXLl0iLCyMQ4cOWTWnaD8mENgZplIoZggDo4gRMXcGYtOy3N0/CWv6wMt7oHCEZlPgnYGQSkKZu6MDMpnByBcVr8XJPaW4++/ef3Rf153QyFAcFY5MbzKdYTWGmRLUjKVQhOVOILB/ypcvz7Vr15gzZw4eHh5ER0fToUMHhgwZQr58afefTg8h7gQCOyPRLZuxMDBa7jQ6CY1OdKmIeTWhQq+Ho3Ph3wmg14JPIHReAvmD0pxDLpfh7uhAlEpLVLyW3O6JMTM6vY6pB6cyYf8E9JKeEr4lWNVpFVXyVUk2R3xCEWNnYbkTCOwajUZDixYtCA4OZsyYMTabV4g7gcDOMHWoMCOhwtkxcUzSllc5lcSECgXEhhl6w17fYXixXHto8zM4e2U4j4ezUdwlZsw+iHzAh39/yL47+wDoWaknc96bg4eTR4rjjc/CSVjuBAK7RqlUcv78eZvPK74WCgR2hiVuWUeFHHmCZ1HE3UFsQocK7+enIbiOQdgpnKDVTOi02CxhBymTKjZf20yl4Ersu7MPN6UbS9st5Y92f6Qq7MCyZygQCN5uPvzwQxYuXGjTOYXlTiCwMyzJlpXJZLgoFcSodTle3Km1erQ6HYMVm8m1Zg1IOshV3OCG9a9g0VzGpIqwmDiGbx/Oz8d+BiDIP4hVnVZRMlfJdI83xkA6K4W4EwjsHa1Wy6JFi/j333+pWrVqihJzM2fOtHhOs8Tdpk2bzJ7w/ffft3gRAoHAdljilgVwcTSIu/gcXg4lNuwRS5TTqa84DxJQ4QNoPRPSsK6lh3uCuPty1wQuxywCYHiN4XzX5DucHDKuWxWfINCdzYibFAgEbzcXL16kShVD3O21a9eSvZalvWXbtWtn1mQymQydLmd/QAgE2Y0lpVAgSa27nGy5u/MfHmv6Ul/xhDjJEZe2P0LQR6lmw5rDi7j7gBOhL5+TyyMXS9otoXXJ1mYfr0p4Fk7CciewIXq9noiICLPGent7Z+laBIns3bvX5nOaJe70ehFoLRC8LVjiloWkLchy4N+5XgcHf4R901BIeq7rC/C1w+esqdLTqumiVFEM2TqEww888eA9inqVY8vAeRTwLGDRPMJyJ8gKIiIimLn5DM5u6Vuj42OiGNk67YxwwZuPiLkTCOwMVUIZDXOD8V0cc6jlLuoJrB8At/cD8KJEJ96/0JJcbj4ZHJg6px+dpuvarlwPu46PrA8AHUp1t1jYgYi5E2Qdzm4euHl6Z/cyBFmMVeIuJiaG/fv3c+/ePdRqdbLXhg0bZpOFCQQC67DUcudsstzlIHF3ax+sGwAxT0HpCq1mEuLWlLgLx3DPqK/sK0iSxOxjsxn972jUOjUBngF0CezOmmMqolXW3VNRCkUgEGQGi8XdmTNnaNmyJbGxscTExODr68vz589xdXUlT548QtwJBNmMtW7ZOI0O5yxb1RuCTgv7v4cDPwAS5ClryIb1K0XMpccAuDqaL6iexz6nz8Y+bL5maEnWrnQ7Fr6/kM1nI1nDJbP6y6aGpdZXgUAgSIrF7xwjRoygTZs2hIeH4+LiwtGjR7l79y5Vq1ZlxowZWbFGgUBgARZny5rEnZ3H3EU+gqXvw4HpgARVesGAPeBXCkjSncJMy92+O/uoFFyJzdc246RwYs57c1j/wXp8XXxNpVCsF3fGmDsh7gQCe6RKlSqEh4cDMGnSJGJjY206v8XvHGfPnuXzzz9HLpejUChQqVQEBAQwffp0vv76a5suTiAQWI6lBXCNMXd27Za9/i8E14a7h8DRHTosgPdng9LFNMTUncIxfXGn1WsZt3ccjf5oxMOoh5TOXZpj/Y8x5J0hprIFHk7GIsaa9KZKE9FbViCwb0JCQoiJiQFg4sSJREdH23R+i92ySqUSudzwoZEnTx7u3btHmTJl8PLyIjQ01KaLEwgElmNtzF2cPda502lh7xT47yfDdt4KBjds7uIphsYmWO5cndIWVKEvQ+m+vjv/3fsPgL6V+zL7vdm4OSYvOpppy50ohSIQ2DWVK1emT58+1KlTB0mSmDFjBu7u7qmOHTdunMXzWyzugoKCOHHiBCVKlKB+/fqMGzeO58+fs2zZMsqXL2/xAgQCgW0xuWVzeimUl/dhbT8IPWrYrt4fmn0LytQjC01u2TQsdxuubKDvxr6Ex4fj4ejB/Nbz6VahW6pjje3HIq0Ud6IUikBg3yxZsoTx48ezefNmZDIZ27Ztw8Eh5XuPTCZ7PeJu6tSpREVFAfDtt9/Ss2dPBg8eTIkSJVi0aJHFCxAIBLbFVMRYYZ4wcHE0iMA4jQ6sq9n75nF1O2wYBHHh4ORpcMGWa5/uIUa37KuWu3htPKN2jmLuibkAVM9fnZUdV1LMt1iacyVa7qx1y4pSKAKBPVOqVClWrVoFgFwuZ/fu3eTJk8dm81ss7qpVq2b6f548edi+fbvNFiMQCDKPytKYu6SlUByzbFmvB60adk+EI3MM2/kqQ+fF4Fs0w0Nj1QYrm3sSy13IsxC6ruvK+SfnAfhfrf8xpdEUHBXp3yjPBMudSqtHrdWbbUU1ohKWO4Egx5AVjSJEEWOBwM6wOububRd34XdhbV94cNKwXWMwNJ0IZvRyBUw16VydHJAkicVnF/Pptk+J1cTi5+rH0vZLaVG8hVlzGXvLgsF6l8vdvDUYiRcxdwJBjuLmzZvMmjWLkJAQAMqWLctnn31GsWJpewjSw2JxV6RIkXQb2d66dcuqhQgEAtugTqiRZnbMnaMdxNyFbIaNn0D8S3D2grbzoIz5vVwhMaFCLtfQY30PVl5cCUCTok1Y2m4p+TzymT2XQi7D1VFBrFpHrFpHLgvWIUmSxdZXgUDw9rJjxw7ef/99KleuTO3atQE4dOgQ5cqV459//qFp06YWz2mxuBs+fHiybY1Gw5kzZ9i+fTv/+9//LF6AQCCwLZYmVLi+ze3HtCrYNQ6OBRu2C1SDTovAp7DFU8UkuGUnHfyaO/HrUMgUTGk0hdG1RyOXWS6yXB0diFXrTPOai0YnoZcM/xelUAQC++fLL79kxIgRfPfddyn2f/HFF69H3H322Wep7p87dy4nT560eAECgcC2mNyyFhYxfuvq3IXdgjV94NFZw3bNodB4PDhY7lvWS3puvngAuPAk5i6FfQuzsuNKagbUtHp5bk4KnkdDjIUtyOK1ieNFEWOBwP4JCQlh9erVKfb37duXWbNmWTWnzd453nvvPdatW2er6QQCgZVYWsQ4Wczd28Klv2F+fYOwc/GBbn9B82+tEnZPY57ScnlLHkY+B6Be4LucHXQ2U8IODJY7SEzUMBejyJYhWZyIIRAI3j78/Pw4e/Zsiv1nz561OoPWZgkVa9euxdfX11bTCQQCK7G6t6z6LYi508TDjq/h5ELDdsC70GkheBW0arp/b/3LR39/xOPoxxSkOwA/NJuMt7N3ppdqdHdbarlTJcQ+OshJN75ZIBDYBwMGDGDgwIHcunWLWrVqAYaYu++//56RI0daNadVRYyTvuFIksTjx4959uwZ8+bNs2oRAoHAdhhj7pzMLKPh8rbE3D2/AWt6w5MLhu06I6Hh16BQWjyVRqdh/L7xfPffd0hIlPMrh/5xbmLVktm9ZTPCKO6stdyJRFmBIGcwduxYPDw8+PHHH/nqq68AyJ8/PxMmTGDYsGFWzWnxu1jbtm2TiTu5XI6fnx8NGjSgdOnSVi1CIBDYBr1eQqMzRONb3qHiDRZ359fA5uGgjgbX3NBhPhRvYtVUdyLu0G1dN47eN3Su+Ljqx/zY7EcqjN8HYDNxZ+x0EWNhWzdj1rIQdwJBzkAmkzFixAhGjBhhahLh4eGRqTktfhebMGFCpk4oEAiyDqPVDqysc/emoY6F7V/A6aWG7cJ1oOMC8DS/LElS1lxaw4B/BvBS9RIvJy8WvL+ATmU7Ea/RmTJUbWa5S+h0YSyxYi7GhAql8MgKBDmOzIo6Ixa/iykUCh49epQiyO/FixfkyZMHne4N/IAQCHIIxvpoYEG2bJI6d0aB80bw7KrBDfv0MiCD+qOh3mhQWC6+YjWxjNg+gt9O/wZAzYI1WdFxBYHegQBEJxFgLjYqP2Kt5U4lLHcCgSCTWPwuKUmpv/urVCocHd/m8vYCwduPOom4UyrMM/0kFTPaNyWn4uwK2PI5aGLBLQ90/B2KNrBqqotPL9J1bVcuPbuEDBlf1vmSiQ0mokwSqxebkPTgolSgkNvGZGa15U7E3AkEgkxitribPXs2YPANL1iwAHd3d9NrOp2OAwcOiJg7gSCbSVrA2NxMy6SFcrM9YVYdA1tGwbkVhu0i9aHD7+CR1+KpJEnit1O/MXzHcOK18fi7+7Os/TKaFE0Zq2csNGwrlywkWu5iLXR3m9yyQtwJBAIrMfud7KeffgIMb5jBwcEoFIkfCI6OjgQGBhIcHGz7FQoEArMx1bgz0yULhlZZjg5y1Fp99oq7J5dhTS94fg1kcmjwFdT9HOSWu0nD48IZ8M8A1oUYam+2KN6CP9r9QR631GtGxaiM4s52HSFM2bIWW+6Mbtk3yUcuEAiyAo1GQ4sWLQgODqZEiRI2m9dscXf79m0AGjZsyPr16/Hx8bHZIgQCgW2wtMadERelArVWT7a0l5UkQ8LEttGgjQePfIakicA6Vk13JPQI3dZ14+7LuzjIHfiu8XeMqDki3RZixrg4Y+FhW2C0AlqeLSssdwJBTkGpVHL+/Hmbz2vx28fevXuFsBMI3lAs7U5hxBh3Z6EOyTyqKFg/AP4ZZhB2xRrDoP+sEnY6vY6pB6dSd3Fd7r68S1Gfohzue5jPa32eYW9Yo3XNPSssd6LOnUAgSIcPP/yQhQsX2nROi7+mduzYkXfeeYcvvvgi2f7p06dz4sQJ1qxZY7PFCQQCy1AlxGtZbLlLECKv1S376Dys7QMvboBMAY2+gdrDQW65qnkU9YiP/v6I3bd3A9CtfDeCWwfj6eRp1vHGbFmbWu6M2bKWdqjQimxZgSAnodVqWbRoEf/++y9Vq1bFzc0t2eszZ860eE6L38kOHDiQaq279957jx9//NHiBQgEAtthrVvWmFSh0b+G4mqSZGgftv1r0KnAswB0WgSF3rVqum3Xt9FrQy+exT7DVenKnPfm0Ltyb4tad8UmmCxtGnPnZJ3lTiUsdwJBjuLixYtUqVIFgGvXriV7zdoWhBaLu+jo6FRLniiVSiIjI61ahEAgsA0qnbUxd4bxWW65i38J/3wGl/42bJdsAe1+BVfL+1KrdWq+3v01Px4xfKmsmLcif3X6i9K5Lc/aN2XLvgGWu3hhuRMIchR79+61+ZwWv31UqFCBv/76K8X+VatWUbZsWZssSiAQWIfJcmdBtiwkcctmZczdwzMwv55B2MkdoNm30G2VVcLuZthNai+qbRJ2Q6sP5Vj/Y1YJO0iaLWvLhAoRcycQCMznxo0b7Nixg7i4OCDtusLmYPE72dixY+nQoQM3b96kUaNGAOzevZuVK1eKeDuBIJvJTLYskDXZspIEx3+Dnd+ATg1ehaDzYihYzarpVlxYwaDNg4hSR+Hj7MOitotoV7pdppZotK4ZkyBsgYvVvWWN4k6UQhEIcgIvXrzggw8+YO/evchkMq5fv07RokXp168fPj4+VoW8WfzdsE2bNmzYsIEbN27wySef8Pnnn3P//n3+/fdf2rVrZ/ECBAKB7UgUd5aJFGPMnc3dsnHh8NeHhjInOjWUbg2DDlgl7KLV0fTZ2Ice63sQpY6iTqE6nBt0LtPCDhKta7YtYpxwT7V6NDrzb2y8aD8mEOQoRowYgVKp5N69e7i6upr2d+nShe3bt1s1p1XvZK1ataJVq1Yp9l+8eJHy5ctbtRCBQJB5TB0qLHXLZoW4u38S1vSBl/dAroRmU6DGx2BFgPDZx2fpurYrV19cRS6T803dbxhbfywOctuIMaPlzs2Glrukmbexah1eLuY9E+GWFQhyFjt37mTHjh0ULFgw2f4SJUpw9+5dq+bM9DtjVFQUK1euZMGCBZw6dQqd7nUXyhIIBEZMde4sVAZGd6RGZ4NsWUmCI3Pg3wmg14JPIHRaDAWqWDGVxNwTc/l85+eodWrye+RneYflNAhskPl1JsGYUOFqQ8udo4McpUKGRicRq9bi5aLM+CASEyochbgTCHIEMTExySx2RsLCwnBycrJqTqvfPg4cOEDPnj3Jly8fM2bMoFGjRhw9etTa6QQCgQ0w1rmzpP0YgLOt6tzFhsHKrob4Or0WyraDjw9YJexexL6g/V/t+XTbp6h1alqXbM25QedsLuwAYhMsd+42FHeQaL2zJGPWWArFwrBJgUDwllK3bl2WLl1q2pbJZOj1eqZPn07Dhg2tmtOid7LHjx+zZMkSFi5cSGRkJB988AEqlYoNGzaITFmB4A0gswkVmRJ3947C2n4QeR8UTtBiKlTrZ5Ub9uDdg3Rf3537kfdxVDjyQ9Mf+PSdT62u+ZQRJsudDd2yYHDzvozTWJQxK0qhCAQ5i+nTp9O4cWNOnjyJWq1m9OjRXLp0ibCwMA4dOmTVnGaLuzZt2nDgwAFatWrFrFmzaNGiBQqFguDgYKtOLBAIbE+2iDu9Hg7Ngj1TQNKBbzHovATyVbR4Kp1ex5QDU5h0YBJ6SU8J3xL81ekvgvIFWbEw88mKUiiQ6Oa1xnInxJ0gI/R6PRERERmO8/b2Rm5F5xfB66F8+fJcu3aNOXPm4OHhQXR0NB06dGDIkCHky5fPqjnNfifbtm0bw4YNY/DgwZQoUcKqkwkEgqxFZW1Cxf/bu+/4pqo2gOO/JG3STSmrgCwFmZUpWHmZIoigIKCA7KUoKENZDoqgMhREkb31BcvLFAHBCpQhSxFUZCh7ll26M+/7R0jaQmmTNmlL+nw/Hz56k3tPTnJp+vCcc55jn3Pn5Asm3oC1r8PJn63HYS9D2y9AF+hkQ3Ax7iLd13Rnx7kdAPSq2Yuvn/+aAG2A0205y1auxJVFjK3tWT/XZKMTmTsphSIcFBsby7QNh/Dxf/DPW0piPMPb1iYkxPl6kiL3FCpUiPfff99l7Tn8TbZ7924WLlxI3bp1qVq1Kj169KBLly4u64gQIudyuv2YU5m7s7/A6n4QfwW8fOD5z6B2j2wNw/5w4gf6fN+Hm8k3CdAGMLvNbLo/0d3pdrIryZ65c+2wbHbm3NlKociCCuEIH/9A/IOC87obIodu377NwoULOXbsGADVqlWjT58+2Q7KHf76eOqpp5g/fz5Xrlzh9ddfJzIyklKlSmGxWIiKiiI+Pj5bHRBCuE6uDMtazLDjM1ja1hrYFX0cBmyHOj2dDuz0Jj1DfhzCi5EvcjP5JnVK1uH3137P1cDOYlHsmTs/V2fusrFLRYpJhmWFKEh27txJ+fLl+eqrr7h9+za3b9/mq6++okKFCuzcuTNbbTr99eHv70/fvn3ZvXs3f/31F++88w6TJk2iePHivPjii9nqhBDCNXK+Q0UWwVn8Vfj2Jdj+MSgWqPkqvBYNJZxfUPXPzX8IXxjOVwe+AmDYU8PY03cPlYrk7rSP5DRj0flhtazUuROiYBk0aBCdO3fmzJkzrFmzhjVr1nD69Gm6dOnCoEGDstVmjr4+KleuzJQpU7h48SLfffddTpoSQriArYixzskdKhzaW/Z0NMz5D5zZAd5+0H42vDQbtP5O9/ObP76hztw6HIo5RFG/omx8dSPTWk1D55W9mk45YVspq1KBj4sjKmczd4qiyA4VQrjJpEmTUKlUDB061P5YSkoKgwYNokiRIgQEBNCxY0euXr2a7rrz58/Tpk0b/Pz8KF68OCNGjMBkSv8zHR0dTZ06ddDpdFSsWJElS5Y43K+TJ0/yzjvvoNGkfm9rNBqGDx/OyZMns/VeXfL1odFoaN++PevXr3dFc0KIbNIb3TDnzmKG7Z/CN+0h8RoUr2Ydhq31qtP9i9fH02NtD3qt60WiMZFm5Zvxx8A/eL7S80635Sqpu1N4ubzUip+T+8sa0mxTJnXuhHCdX3/9lblz5/LEE+lX8Q8bNowffviBlStXsmPHDi5fvkyHDh3sz5vNZtq0aYPBYGDPnj0sXbqUJUuWMHbsWPs5Z86coU2bNjRr1ozDhw8zdOhQ+vfvz5YtWxzqW506dexz7dI6duwYNWvWzNb7de0YhBAiT9kzd9ncfsx4b3AXdwVW94dzu63HdXrCc5NBe3819awcvHyQLqu7cPLWSTQqDR81/YjR/xmNRu3aRQzOspVBcXWNO0hdLWtbsJGVlDQ3QBZUCOEaCQkJdOvWjfnz5/Pxxx/bH79z5w4LFy5k+fLlNG/eHIDFixdTtWpV9u3bx1NPPcVPP/3E0aNH+fnnnylRogS1atViwoQJjBo1inHjxqHVapkzZw4VKlRg6tSpAFStWpXdu3fzxRdf0KpVqwz79Oeff9r//+2332bIkCGcPHmSp556CoB9+/Yxc+ZMJk2alK33LMGdEB4k23PuMtqh4uTPsOZ1SLoB2gBoOx2eeNnpPimKwvR90xn18yiMFiNlgsrwXcfvaFi2odNtuUOSwT27U0CaOncOZu5sNe5UKtC4p16zEA+9+Ph44uLi7Mc6nS7TbboGDRpEmzZtaNGiRbrg7uDBgxiNRlq0aGF/rEqVKpQtW5a9e/fy1FNPsXfvXsLCwihRooT9nFatWvHGG2/w999/U7t2bfbu3ZuuDds5aYd/71WrVi1UKhWKklryaOTIkfed9+qrr9K5c+cHtvMg+SK4mzlzJp999hkxMTHUrFmTGTNmUL9+/Syvi4yMpGvXrrRr145169a5v6NC5HMuWS1rMcHPn8DuL6xPlgizFiUuWtHp/lxPvE6f7/uw8d+NALxU5SUWvLiAEN/8U3PLnrlzcRkUSM0GOjrnzpa58/FSZ6eijBAFwr07YkVERDBu3LgMz42MjOT333/n119/ve+5mJgYtFotwcHB6R4vUaIEMTEx9nPSBna2523PZXZOXFwcycnJ+Pr63vfaZ86cefAbdIE8D+5WrFjB8OHDmTNnDg0aNGD69Om0atWKEydOULx48Qded/bsWd59910aNWqUi70VIn/LdhHju8FdCeUW6m9fhIsHrE/U6wetPgVvH6f7sv3Mdrqt6caVhCvoNDq+aPUFA+sNdNsWYtmVuvWYGzJ3Tq6WtZVBsc2BFELc7+jRo5QuXdp+/KCs3YULFxgyZAhRUVH4+Dj/HeZO5cqVc2v7eR7cTZs2jQEDBtCnTx8A5syZw8aNG1m0aBGjR4/O8Bqz2Uy3bt346KOP2LVrl0PbrwhREGS7iLFWTTP1IaZ5z0ZzMQF0QfDCl1CjQ9YX38NkMfFR9Ed8susTFBSqFq1KZKdInijh/HZkuSFJ775hWX+nM3fWvuhkNYUQDxQYGEhQUFCW5x08eJBr165Rp04d+2Nms5mdO3fy9ddfs2XLFgwGA7Gxsemyd1evXiU0NBSA0NBQDhw4kK5d22ratOfcu8L26tWrBAUFZZi1y8jly5fZvXs3165dw2JJP/n57bffdqiNtPI0uDMYDBw8eJAxY8bYH1Or1bRo0YK9e/c+8Lrx48dTvHhx+vXrx65duzJ9Db1ej16vtx/bii0bjUaMRmMO34HICdvnL/fBdWxzttRYHP9czUa8tn/MYu1MaxvFwlC/vAgKVwAn7835O+fp+X1P9lzcA0Cfmn2Y9uw0/LX++fY+30m2fj/4eqld3kfbSG9CismhthOSDdbr7gZ3+fUzK0jy8/eU0WhEUcxYLA/ODCuK2f77zpHz015j+39Hz8/OazjzuTp7D5555hn++uuvdI/16dOHKlWqMGrUKMqUKYO3tzdbt26lY8eOAJw4cYLz588THh4OQHh4OJ988gnXrl2zjyZGRUURFBRkHx4ODw9n06ZN6V4nKirK3kZWlixZwuuvv45Wq6VIkSLpRjdUKtXDF9zduHEDs9mc4Vj18ePHM7zGtg3a4cOHHXqNiRMn8tFHH933+M6dOzl69KjTfRauFxUVlddd8Bh34jWAioMH9nHz/pX19/E13KDemZmEJJ0CYLGpFYS8QsjeY4ADDaSxN3YvX1/4mkRzIn5qP94o8waNVI3Y8fMO599ILjp8UQVouHntMps2XXRp26fjALy4Hht335d/Ro7HWvtiSEkC5GcjP8mP9yI+Pp5Tl9RZ7i0blXKKwMBAh85Pew3g1PnZeY3AQMf3ob5x44bD54I1w1ejRo10j/n7+1OkSBH74/369WP48OGEhIQQFBTEW2+9RXh4uH3VasuWLalWrRo9evRgypQpxMTE8MEHHzBo0CD7cPDAgQP5+uuvGTlyJH379mXbtm3873//Y+PGjQ7188MPP2Ts2LGMGTMGtdo1Wfs8H5Z1Rnx8PD169GD+/PkULVrUoWvGjBnD8OHD7ceXLl2iWrVqNG7cmPLly7upp8IRRqORqKgonn32Wby9vfO6Ox7hkyM7QK+naeP/UK1k5sMWqhOb0Gz4CFXKHRSfQowwDGCVqQ7rnnqS6o8Udvg1k43JjNw6krln5wLwZKkn+bbdtzxa+NEcvZfccvSnf+HCGao8Vp7nn6/i0raPXYnny7/3ovLS8fzzTbM8X3fsGhw7TNHgIOC2/GzkA/n5e+rWrVuc2XUav8DgB56TFB/Ls40eJSQkxKHz014DOHV+dl7Dmb1Tz5496/C5jvriiy9Qq9V07NgRvV5Pq1atmDVrlv15jUbDhg0beOONNwgPD8ff359evXoxfvx4+zkVKlRg48aNDBs2jC+//JJHHnmEBQsWPLAMyr2SkpLo0qWLywI7yOPgrmjRomg0mgzHqm1j2WmdOnWKs2fP8sILL9gfs41Ne3l5ceLECR577LF019y7RNq2fNrb2zvf/aAWVHIvXMdW587fR/vgz9Skh6gI2D/bely6LqpOi9k39yQkpWBUVA7fj2PXj9F5VWf+umYd+hj59EgmNJ+AVqPN8XvJLcl35ykG+mbymWVTIX/rd0+SwexQ20bFOhzje3chhvxs5B/58V54e3ujUmlQZ1IrUqXS2PvuyPlpr7H9v6PnZ+c1nPlMXfH5R0dHpzv28fFh5syZzJw584HXlCtXLsvMe9OmTTl06FC2+tSvXz9Wrlz5wHUG2ZGnwZ1Wq6Vu3bps3bqV9u3bA9ZgbevWrQwePPi+86tUqXLf+PkHH3xAfHw8X375JWXKlMmNbguRb9kXVGge8MV66wys7A1XDluPwwfDMxHgpcVXa12an3JfJeP7KYrCwkMLefvHt0k2JVPcvzjftP+GVhUd+5dqfmJbyerO1bJJRjOKomS5Uti2oMLV26AJIfKviRMn0rZtWzZv3kxYWNh9Qey0adOcbjPPh2WHDx9Or169qFevHvXr12f69OkkJibaV8/27NmT0qVLM3HiRHx8fO4bP7etcLn3cSEKokxXy/69Dta/Bfo48C1s3Ru2cmv707ZyKMnGzCdC30m5w+sbXmfF3ysAePbRZ/nmpW8IDbg/2/4wsK1kDXBDnTvb3rKKYg2afbPYBSPFlL29gYUQD6+JEyeyZcsWKleuDHDfgorsyPPgrnPnzly/fp2xY8cSExNDrVq12Lx5s32Rxfnz5106Di2Ep7JYFEwWa7XzdMGdMQV+eh9+XWA9LtMAOi2CQo+ku95WWy0lk+Bu/8X9dF3dlTOxZ/BSe/Fxs48Z0XAEatXD+zOaoHdfnTsfLw0qlTW4SzSYsgzu9JK5E6LAmTp1KosWLaJ3794uazPPgzuAwYMHZzgMC/ePj99ryZIlru+QEA+htJvO24O7m6dgZS+IuTud4T/DoNn7oLl/7kpmmTuLYuHzPZ/z/rb3MVlMlA8uz3cdv+OpR55y/RvJZbbtx/zdkLlTq1X4eWtINJit9fQCMj8/dVhWMndCFBQ6nY6GDV27HaP881AID6E3pQnuNGr4axXMbWwN7PyKQLfV0GJchoEdpGaLku/ZB/VqwlVaL2vNqJ9HYbKYeLnayxx6/ZBHBHaQuv2YvxuKGEPa/WWzLmRsu4c+UsRYiAJjyJAhzJgxw6Vt5ovMnRAi52zz7XxUBrw3DYXfl1qfKNcQOi6AoFKZXp+auUsNEqNORdFjbQ+uJl7F18uXL5/7kv51+ue7LcRywp3bj4F1l4rrOLZLhS1zp/VSQ9brWoQQHuDAgQNs27aNDRs2UL169fsWVKxZs8bpNiW4E8JD6E1mHlNdYpb2K1S/XwBU0HgENBkFmqx/1H20qcOyRrORD7d/yORfJgNQvVh1VnRaQfXi1d35FvKEbfsxdwzLgnP7y9pWKvt4a0CfxclCCI8QHBxMhw7Ob/WYGQnuhPAQ2iMr+EH7AX4qPfgXhw7z4LFmDl9vy9xdjb9Fo8W92X9pPwAD6w5kWqtp+Ho7tkfiw8aWufN3V+ZO5/j+svY5d15qCe6EKCAWL17s8jYluBPiYWdIhE0jKH54GajggKoG9QeugcASWV+bhm3O3dI/vuOqZj/BPsEseGEBHat1dEev8wWT2WLPlrlrzp2vM5k7WykUWVAhhMgBCe6EeJhdPWotSnzjBIpKzTRDB9b6d2a3k4FdkjGJn05vAKpjNKsILxfOdx2/o1xwObd0O79ISrMy2C+LMiXZ5a/NRuZOSqEIUWBUqFAh03nMp0+fdrpNCe6EeBgpChz6FjaNBFMyBITyT8MvmPG9Qnknt+j56+pfdFndhQsxFQmhOtWL1mZr76l4P2BVrSexzbfzUqvQuWmFqn3OncGROXe2YVnJ3AlRUAwdOjTdsdFo5NChQ2zevJkRI0Zkq00J7oR42OjjYcNw+Ot/1uPHmsNL87gZowL2Z7w7RQYURWHuwbkM2zKMFFMKpXQ1wAiPF65eIAI7SFvAWOO2FcD2OXd6B0qh2BdUqFHc0hshRH4zZMiQDB+fOXMmv/32W7balNy/EA+TmL9gXlNrYKfSWPeF7bYaAoqhN2ey9dg9biff5uWVL/PGxjdIMaXQumJrJjT9AEhfCsXT2YZK3TXfDpzL3OlNaUqhCCEKtNatW7N69epsXSvfIEI8DBQFfl0I85+BmychqDT03giNhsPd7fkMDu5LuufCHmrNrcXqY6vxVnvz+bOfs+HVDRQPKAxkvv2Yp0m0l0FxX3Dn3Jy7NKVQhBAF2qpVqwgJCcnWtTIsK0R+lxIHP7wNf6+1HldqBe1ng3+RdKfZdjfQajL+N5vZYmbyL5MZu30sZsXMo4UfJbJjJE+WfhLAvu9pRtuPeSr77hRuWkwBaXaocGi1bGoplDtu65EQIj+pXbt2umkhiqIQExPD9evXmTVrVrbalOBOiPzs8iFY2QdunwG1l3X7sKcG2bN1adkydxkN6V2Ov0yPtT3YdmYbAK+GvcrsNrMJ0gXZz7Gt0CxQmTs3704B2V0tK5k7IQqK9u3bpztWq9UUK1aMpk2bUqVKlWy1KcGdEPmRosCBefDTB2A2QKGy0GkRlHnygZc8KLj78d8f6bmuJzeSbuDn7cfM52fSq2av+xYQZLT9mKdLMrh/WNaWuUtyaLWsbWhdZswIUVBERES4vE0J7oTIb5Jvw/eD4fgG63GVttDua/AtnOllhnsm4xvMBsb8PIZp+6YBULNETSI7RVKlaMb/ErRliwpU5s42LOumrccgNXPnVCkUydwJIXJAgjsh8pOLB2FVb4g9D2pvaPkxNHgdHCjTYbi7WlanUXPy1km6ru7Kb5ety+jfqv8WU56dgo+XzwOvT83cFaTgzvpe3Tksa2s7q1IoiqLY501KEWMhPJ9arc6yBJNKpcJkynpKx70kuBMiP1AU2DsTfo4AiwkKl4dOi6F0HYebsA3Lno87Q+25PUkwJBDiG8KiFxfRrkq7LK/3tc+5s2CxKKjV7qn7lp/Y5sEFuDNzZ99bNvOg2RbYQdYrnoUQD7+1a9c+8Lm9e/fy1VdfYbFkb5qMBHdC5LWkW7DuTfjnR+txtXbw4gzwKeRUMwn6FAC2nd1CgjaBRmUbsazDMsoUKuPQ9b5pVozqTZZ0x54qtYhxbtS5y/xf33pj2uBOMndCeLp27e7/R/eJEycYPXo0P/zwA926dWP8+PHZalu+QYTIS+f3w5xG1sBOo4M2U+HlpU4HdodjDjP/4BLrgcpERJMItvXa5nBgB+m3vCooQ7OpCypyIXOXRSkUWxkUtQq8NZ6fNRVCpLp8+TIDBgwgLCwMk8nE4cOHWbp0KeXKZW9/bwnuhMgLFgvs/gIWt4a4ixDyGPT/GZ7s79D8OhtFUZixfwYNFjTgdnICAN3CXmFc03F4qZ3LRqnVKrxV1k2vCkpwl7qgwv2ZO4PZYh86z0jaxRTu2gpNCJG/3Llzh1GjRlGxYkX+/vtvtm7dyg8//ECNGjVy1K4MywqR2xJvwNqBcDLKelyjE7wwHXSBTjVzM+kmfdf3Zf2J9QBUD67IrZtQsUiFbHfNWwNGEyQ7UJPNE9gzd24dlk2TETWYH7i1mOxOIUTBMmXKFCZPnkxoaCjfffddhsO02SXBnRC56ewvsLofxF8BLx9oPRnq9HIqWwew89xOuq3pxsW4i2g1Wj5/9nPOnW/EqpuXcrQvqVYNSUCyoWDUukudc+e+gMpbo0arUWMwW0g0mCjk553hefbMncy3E6JAGD16NL6+vlSsWJGlS5eydOnSDM9bs2aN021LcCdEbrCYYdc0iP4UFAsUfRxeXgIlqjvVjNli5uOdHzN+53gsioXHizxOZMdIapeszdunDwE523Ree/fSgjIsa1st685hWQA/nQZDkiXTXSqkxp0QBUvPnj3dNgVDgjsh3C3hGqwZAKejrcc1u8Lzn4MuwKlmLsZdpNuabuw8txOA3rV6M6P1DAK01nYy237MUd4FLLiz1blzd3Dnr/UiNsmY6f6ytlIoOgnuhCgQlixZ4ra2JbgTwp1O77AGdglXwdvPGtTV7uZ0M+tPrKfP9324lXyLAG0Ac9rModsT6dtJW8Q4u2yjk8kO7KbgCWzlSfzdXPbFz75LRdaZOymDIoTIKQnuhHAHixl2TIYdUwAFilW1DsMWd24T6BRTCiOjRjLjwAwA6pasS2SnSCqGVLzvXIM985OTzJ0CqArMFmS28iR+bh+Wte1S8eDPNUV2pxBCuIgEd0K4WtwVa7bu7C7rce0e0HoKaP2caubEjRN0Wd2FwzGHARj+1HAmtpiIVqPN8Hz7sGxOMncFaFjWYLLYs50BblwtC2n3l5U5d0II95PgTghXOrkV1rwGSTfA299a4uSJV5xqQlEUlv6xlMGbBpNoTKSoX1GWtl/K85Wez/Q6/d0iuC5ZUFEAhmXTLm5w924c9v1lM/lc9fbVshLcCSFyRoI7IVzBbILtn8DuadbjEmHWYdii9w+fZiZOH8cbG99g+V/LAWhWvhn/7fBfSgWWyvJavSyocEri3UBLq1Hn6DNzhCP7y6bWuZNhWSFEzkhwJ0RO3blkrV13fq/1uF5faPUpePs61cxvl3+jy6ounLp9Co1Kw0dNP2L0f0ajUTuWybENMeZoWPbuSxWEOXdJ9t0p3J8ps2fu9DIsK4RwPwnuhMiJf36Cta9D8i3QBsKLX0GNDk41YVEsTN83ndE/j8ZoMVK2UFmWd1hOw7INnWrHFaVQCtKwbGoBY/d/DabOuctsQYUEd0II15DgTojsMBth63jY85X1uGRN6LQYijzmVDPXEq/Re11vfjz5IwAdqnZgwQsLKOxb2OkuuabOXcHZW9a+9VhuZO5sq2UzWVChN+Z8tbMQQoAEd0I4L/Y8rOoLF3+1Htd/HVpOAC+dU81sO7ON7mu6cyXhCjqNjunPTef1uq9nu2K5vc6d7FDhkER97uxOAWkyd5mWQrHVuZPMnRAiZyS4E8IZxzfCujchJRZ0haDd11DtRaeaMFlMRGyPYOLuiSgoVC1alRWdVhBWIixHXbPXuctBcFCQ5tylFjB2/9egI5k7WVAhhHAVCe6EcITJAFFjYf9s63HputBpERQu71Qz52LP8eqaV9lzYQ8A/Wv3Z/pz0/HX+ue4iy7dfqwAzLmzZdH83FwGBcDP24E5d1IKpUCzWCzExsY6dG5wcDBqtfwjQDyYBHdCZOXWGVjVBy4fsh6HD4ZnIsAr42LCD7L66Gr6/9Cf2JRYgnRBzGs7j841Oruki2aLgslinS8nRYwdY8uiBeTGsKytFEqmq2VtmTsJ7gqi2NhYpm04hI9/YKbnpSTGM7xtbUJCQnKpZ+JhJMGdEJn5ex2sfwv0ceATDC/NgcqtnWoi2ZjM8C3DmXNwDgD1S9cnsmMkFQpXcFk3bVk7cNFqWaMl8xM9QIJ967HcK4WSWeZOb18tKxmZgsrHPxD/oOC87obwABLcCZERYwr89D78usB6XKYBdFwIwWWcaubo9aN0XtWZI9eOADDy6ZF83PxjvDXeLu2uy4I725y7AjAsa69zlxulUOxFjKXOnRDC/SS4E+JeN0/Byt4Q86f1uOFQaP4BOBGQKYrCwkMLefvHt0k2JVPcvzjfvvQtLR9r6ZYu683WwEClAi919lbbAmgLUCmURHsplFxYUGHL3GWyWta2w4hk7oQQOSXBnRBp/bUKfhgChgTwKwIvzYVKzzrVxJ2UO7y24TX+9/f/AHj20Wf55qVvCA0IdUePgTSLKTTqbJdSgdQFFZltk+UpEu1FjN2fKfPXOrJaVkqhCCFcQ4I7IQCMybB5NBxcYj0u+zR0WghBWe/pmta+i/vourorZ2PP4qX24uNmHzOi4QjUKvdmY1yxUhZS59wVhFIotkArVzJ3afaWtVgU1BlkV6UUihDCVSS4E+L6P9Zh2Gt/Aypo/C40GQ0ax388LIqFz375jA+2f4DJYqJ8cHkiO0bS4JEGbut2WqkFjHOW9bElsZKNZhRFyVEWML+zDZHmThHj1NdINpozfE3J3AkhXEWCO1Gw/REJG4aDMRH8i0GHefBYc6eaiEmIoefankSdjgKgc/XOzG07l0I+hdzR4wylFjDOWdbHljQyWxSMZgWtlwcHd/Yixu4Ppny81ahUoCjW180suJMFFUKInJLgThRMhkTYNBIO/9d6XKExdJgPgc7Ni/vp1E/0WNuDa4nX8PXyZUbrGfSt3TfXM156Fw/LgjXDlNP28rPUOXfu/xpUqVT4a71I0JtI0pshg1JmKbKgQgjhIhLciYLn2jHrMOz146BSW4dgG78LasczJgazgQ+2fcBnez4DIKx4GJGdIqlWrJqbOp1Ff9IsqMgJjQo0ahVmi0KK0UwhX9eWbMlPbItGcqOIMVgXbiToTRkuVrFYFPs9lMydECKnJLgTBYeiwKH/wqYRYEqGgFDouAAqNHKqmdO3T9N1dVcOXDoAwBv13mBqy6n4evu6o9cOcdWCCpXKmjlK1Js9fguyBFvmLheKGMPduX3x+gxXzNrmTIItuFNypU9CCM8kwZ0oGPQJsGEY/GUtT8JjzeGleRBQzKlmVhxZwWsbXiNOH0ewTzALX1xIh6od3NBh57hqWBbA11tjDe48eMWsoij2DFpuFDGG1JIrGe1SkXZ1ss5LDRbP/eyFEO4nwZ3wfDF/WYdhb54ElQaavw8Nh4ETG28nGhIZsnkICw8tBKBhmYYs67CMcsHl3NRp59gyPzkdloXUYUFPDu70Jgvmu3vx+udW5s5W6y6D/WVtZVA0ahXeGjVGCe6EEDkgwZ3wXIoCBxfDj6PBrIfAUtBpEZQLd6qZv67+RedVnTl24xgqVLzf6H0imkbgpc4/Pz6uGpYF8L07od+TtyBLTBNg5caCCkgd/s0sc+fjwQtYhBC5J//8dhLClVLirDtN/L3GelypJbSfA/5FHG5CURTm/DaHYVuGoTfrKRVYiv++9F+aVWjmpk5nn6tKoYB1WBY8O3NnG5L18VajycF2bc6wDctmNOcuxSRlUIQQriPBnfA8lw9bh2FvnwG1FzwTAeGDnRqGvZV8i/7r+7P2+FoAnq/0PEvaLaGYv3Nz9HKL4W5w4IrMXUEYlrXVuMutlbKQ+f6yqbtTSHAnhMg5Ce6E51AUODAffnofzAYoVMY6DFumvlPN/HL+F7qu7sqFuAt4q72Z3GIyQ58amq93a3D1ggrAo1fL5maNOxv/zDJ3tt0ppMadEMIFJLgTniE5FtYPhmM/WI8rt4F2X4NfiMNNmC1mJu6eyLjocZgVMxVDKhLZMZK6peq6p88u5MphWVsRXU/eX9aWPfPLhd0pbPx0mWXubHPuJHMnhMg5Ce7Ew+/iQVjVG2LPg9obWk6ABgOtRdscdDn+Mt3XdGf72e0AdH+iO7Oen0WgLoOtBPIhV66WLRhz7nJ/WDazzJ1edqcQQriQBHfi4aUosG8WREWAxQjB5eDlxVDauUzbxn820vv73txIuoG/tz+z2syiZ82ebuq0e7hytayP1jYsa8nizIdXgi1zlxdz7jJZLauTzJ0QwgUkuBMPp6RbsO5N+OdH63HVF+HFGeAb7HATepOeMVvH8MW+LwCoHVqbyE6RPF7kcTd02L3cMueuAGTu/HNxWNZWTy+jOnd6o2TuhBCuI8GdePic3w+r+kLcRdBoodWn8GR/p4ZhT946SZdVXTh45SAAQxoMYXKLyei8dO7qtVulDsvmPFixBXcFY85dXmTupBSKEMK9JLgTDw+LBfZ8BVvHg2KGkEfh5SVQsqZTzfz3z//yxsY3SDAkUMS3CIvbLeaFyi+4p8+5xL6gwgWZH1sRY09eLZs65y4PMneZFTGW4E4I4QIS3ImHQ+INWDsQTkZZj2t0hLbTwSfI4SYSDAkM3jSYpX8sBaBxucYs67CMR4IecUOHc5d9zp0rth/Tev6wbIKtFEqe1Ll78PZjMiwrhHAFCe5E/nduj3UYNv4KePlA68lQp5dTw7CHrhyiy+ou/HPzH9QqNRFNIni/0fto1J6RKdG7sIixbVg2owyTp0i6Oyybq3Pu7gZ3GWVEZUGFEMKVJLgT+ZfFArunwvZPQbFAkUrWYdjQGg43oSgKMw7MYETUCAxmA48EPcKyDstoXK6x+/qdB1y6WrYgzLmzLajIzcxdJnvLppZCkeBOCJFzEtyJ/CnhGqx5DU5b687xRBdoMxV0AQ43cSPpBn2/78sP/1gLG7er3I6FLy6kiJ/j+8s+LGwLKlyzt+zdOXeeHNzpbatlc7POnfW1Mt2hwgX3Twgh5JtE5D+nd8Cc/1gDOy9faDcTXprjVGC34+wOas2pxQ///IBWo+Xr1l+ztvNajwzswLVz7grE9mMGW5273NyhwvpaRrNiv182sresEK43ceJEnnzySQIDAylevDjt27fnxIkT6c5JSUlh0KBBFClShICAADp27MjVq1fTnXP+/HnatGmDn58fxYsXZ8SIEZhM6f+RFh0dTZ06ddDpdFSsWJElS5a4++1lSoI7kX9YzLB9InzTDhKuQrGq8Fo01O7u8Pw6k8VExPYImn/TnEvxl6hcpDL7++9nUP1B+Xpv2JySYVnnJOXFsGyawO3e7F1qKRT5ShbCVXbs2MGgQYPYt28fUVFRGI1GWrZsSWJiov2cYcOG8cMPP7By5Up27NjB5cuX6dChg/15s9lMmzZtMBgM7Nmzh6VLl7JkyRLGjh1rP+fMmTO0adOGZs2acfjwYYYOHUr//v3ZsmVLrr7ftPLFN8nMmTMpX748Pj4+NGjQgAMHDjzw3Pnz59OoUSMKFy5M4cKFadGiRabni4dEfIw1qNsxCVCsAd2AbVC8isNNXLhzgeZLmzN+53gsioU+tfpw8LWD1Aqt5bZu5xdSxNg5ifYFFbkX3Hlp1Pb7c++8O72UQhHC5TZv3kzv3r2pXr06NWvWZMmSJZw/f56DB631Te/cucPChQuZNm0azZs3p27duixevJg9e/awb98+AH766SeOHj3Kf//7X2rVqkXr1q2ZMGECM2fOxGAwADBnzhwqVKjA1KlTqVq1KoMHD6ZTp0588cUXefbe8zy4W7FiBcOHDyciIoLff/+dmjVr0qpVK65du5bh+dHR0XTt2pXt27ezd+9eypQpQ8uWLbl06VIu91y4iur0dpjdEM7uAm9/eGmedShW6+dwG98f/55ac2ux6/wuArWBLO+wnEXtFuGv9Xdjz/OP1Dl3OQ8OfArQnDu/XFwtC2n2l72nHIqUQhHCcfHx8cTFxdn/6PV6h667c+cOACEhIQAcPHgQo9FIixYt7OdUqVKFsmXLsnfvXgD27t1LWFgYJUqUsJ/TqlUr4uLi+Pvvv+3npG3Ddo6tjbyQ598k06ZNY8CAAfTp04dq1aoxZ84c/Pz8WLRoUYbnL1u2jDfffJNatWpRpUoVFixYgMViYevWrbncc5FjFhNVL69E890rkHQDStSA13dAzc4ON5FiSuGtTW/RfkV7biXfol6pehx6/RBdw7q6seP5jyuHZX21nj/nzlbmJSAXh2XhwfvL2osYSykUIbJUrVo1ChUqZP8zceLELK+xWCwMHTqUhg0bUqOGteJCTEwMWq2W4ODgdOeWKFGCmJgY+zlpAzvb87bnMjsnLi6O5OTkbL3HnMrT1bIGg4GDBw8yZswY+2NqtZoWLVo4HPEmJSVhNBrtkfi99Hp9uqg+Pj4eAKPRiNFozEHvRY7EXUa9dgCPX90PgLl2LyzPfgzevuDgfTl+4zjd13Xnz2t/AjCswTAmNJ2AVqMtcPfWNqynVizZfu+267ywBop6kwW93oBa7VlzFRVFsZdC0aqVXP274qe1Bt9xSSnpXjfZaO2P193+2J4raH+P86PcuhdGoxFFMWOxZP6PKkUx2/+OOHKNs+envcb2/67u073XOMp27tGjRyldurT9cZ0u620jBw0axJEjR9i9e7fDr/cwy9Pg7saNG5jN5gwj3uPHjzvUxqhRoyhVqtR9KVGbiRMn8tFHH933+M6dOzl69KjznRY5VvzOH9Q5NxdvcwJGtQ+Hy/blMk9B1HaHrlcUhW23tjHv0jz0Fj2FvArxdtm3qauvy89bfnZz7/OnxGQNoGLfnl2c8c1ZW3t27cD21fD9xh/JxQWluUJvBkWxvr/d0Vtz9f0Zkqz3aeeeA9w+rtgfv3nb+vgfB38j6WTq41FRUbnXOZEpd9+L+Ph4Tl1S4+MfmOl5KYnxRKWcIjAw0KFrnD0/7TWAW/p07zWOunHjBgCBgYEEBTm+O9HgwYPZsGEDO3fu5JFHUnckCg0NxWAwEBsbmy57d/XqVUJDQ+3n3Duv37aaNu05966wvXr1KkFBQfj65vALOZse6jp3kyZNIjIykujoaHx8fDI8Z8yYMQwfPtx+fOnSJapVq0bjxo0pX758LvVUAGA2oo7+BM3prwGwlAhjR5GePN22O7W8vR1qIk4fx+DNg4m8EAlA8/LNWfziYkoGlHRbtx8Go3/7GcwWWjRvSpnCjs9VTMtoNBIVFcXzrVow8kA0AI2bt6CIv9aFPc17NxL0cGAHKhW0a9M6VzOTK67+xrmEW1QNq8XzNVP/zn52fBckJ9O4YTi1ywbb78Wzzz6Lt4M/G8I9cute3Lp1izO7TuMXGJzpeUnxsTzb6FFCQkIcusbZ89NeA7ilT/de46izZ886fC5YEwFvvfUWa9euJTo6mgoVKqR7vm7dunh7e7N161Y6duwIwIkTJzh//jzh4eEAhIeH88knn3Dt2jWKFy8OWAP9oKAgqlWrZj9n06ZN6dqOioqyt5EX8jS4K1q0KBqNJsOI1xYRP8jnn3/OpEmT+Pnnn3niiSceeJ5Op0uXso2LiwPA29tbvjRzU+wF6xZiF+/+C6j+a5ibRZD401aH78Vvl3+jy6ounLp9Co1Kw4RmExjZcKTHbCGWEwazNdvj76PL8d9rnVaLzkuN3mTBpKg87udEb7aucPPz1qDT5W7g6u/jfbcPpPtcbaud/X216R6X76n8w933wtvbG5VKgzqL7zOVSmPviyPXOHt+2mts/+/qPt17jaOc/fwHDRrE8uXL+f777wkMDLTPkStUqBC+vr4UKlSIfv36MXz4cEJCQggKCuKtt94iPDycp556CoCWLVtSrVo1evTowZQpU4iJieGDDz5g0KBB9thi4MCBfP3114wcOZK+ffuybds2/ve//7Fx40an+utKebqgQqvVUrdu3XSLIWyLIzKLeKdMmcKECRPYvHkz9erVy42uipw4vslalPjiAdAVgle+gec/A6+s50kAWBQLU/dM5emFT3Pq9inKFSrHzj47GdNojAR2gNmiYLZYgztXFDGG1EUVnljrzjbfzi+XF1NAmtWy99a5k1IoQrjc7NmzuXPnDk2bNqVkyZL2PytWrLCf88UXX9C2bVs6duxI48aNCQ0NZc2aNfbnNRoNGzZsQKPREB4eTvfu3enZsyfjx4+3n1OhQgU2btxIVFQUNWvWZOrUqSxYsIBWrVrl6vtNK8+HZYcPH06vXr2oV68e9evXZ/r06SQmJtKnTx8AevbsSenSpe2rYSZPnszYsWNZvnw55cuXt0fiAQEBBAQ4voOByAUmA/wcAftmWY9L1YGXF0Ph8g43cS3xGr3W9WLzyc0AdKzakQUvLiDYJ9j1/X1Ipd3tQOeiUhq+3hpiMZJssGR98kMmr1bKQmpAaauzZ6OXHSqEcDlFUbI8x8fHh5kzZzJz5swHnlOuXLn7hl3v1bRpUw4dOuR0H90lz4O7zp07c/36dcaOHUtMTAy1atVi8+bN9kUW58+fR61O/YU1e/ZsDAYDnTp1StdOREQE48aNy82ui8zcPgsr+8Dl363HTw2CFuPAy/FhsK2nt9J9bXdiEmLw8fJheqvpvFb3NY/eaSI70gZ3LsvceXAh44Q8qnEHGWfuzBbFXqfQR/aWFUK4QJ4Hd2BdyTJ48OAMn4uOjk537OyESpEHjn4P378F+jvgEwztZ0OV5x2+3Gg2Mi56HBN3T0RBoVqxaqzotIIaxWu4r88PMb35bhkUlXUXBFfw8eDgLikPdqewSa1zlxrc6U2pn7Fk7oQQrpAvgjvhIYwp8NMH8Ot86/Ej9aHTIggu43AT52LP0XV1V/ZetNY5fK3Oa3zx3Bf4eWdvBWhBYBvSc0UBYxtPLmScaN9XNg8ydzpb5i71c7XdPwCdZO6EEC4gwZ1wjZunYGVviLEWFKbhEGj+IWgcX920+uhq+v/Qn9iUWArpCjHvhXm8Uv0V9/TXg9iG9Fw1JAupQ5YeuaBCn3cLKmyZu6Q0c+5S7mbuvNQql2VehRAFmwR3Iuf+WgU/DAVDPPiGwEtz4fGWDl+ebEzmrS1vMffgXAAalG7Adx2/o0LhCllcKSDt1mOuy0R59LCswTYsm3eZu7TDsimymEII4WIS3InsMybD5tFwcIn1uOzT0HEBFCqd6WVpnU8+z9NLnubv63+jQsWohqMY32w83k5k/Ao6W3DnyiE924KKJE8cltXbhmXzMHOX5nNNLYMiWTshhGtIcCey58a/1mHYq0cAFTR6B5qOAY1jf6UURWHhoYW8+8+7GBQDJfxL8O1L3/LsY8+6tdueyD4s64bgzpOHZfNmQYUmXR8g9TPWuTDzKoQo2CS4E877YwVsGAbGRPAvBh3mwWPNHb48NiWW1354jZVHVwLQ8tGWfPPSN5QIKJHFlSIjbsncefSCCut78suDBRUZZ+5sw7KSuRNCuIYEd8JxhiTYNAIO/9d6XL6RdRg2MPOt4tLad3EfXVd35WzsWbzUXnQL7cbcznPRaR3brULcL3XOneuCA8+ec2fNmuVFEePU1bJpMncm2Z1CCOFaEtwJx1w7Zh2GvX4cUEHT0dB4BDi4/ZdFsfDZL5/xwfYPMFlMPFr4Ub5t9y3XD19HrZKMRU7Y9iV15WpZzy5ifDdzlwfDsrah4LQ7VMjuFEIIV5PgTmROUeDwMtj4LpiSIaCENVtXobHDTcQkxNBzbU+iTkcB0Ll6Z+a2nYufxo9NhzPf0kVkzVYE17V17qxtpXjgsGySfc5dXgzLpgbNZouCRq2y3z8ZlhVCuIoEd+LB9AmwcTj8eXeT5UebQYf5EFDM4Sa2nNxCz3U9uZZ4DV8vX2a0nkHf2n1RqVQYjUY3dbxgccewrCdn7mxz7vJitWza10w2mgnQecmCCiGEy0lwJzIWc8Q6DHvzX1Cpodn78J/hoHYsgDCYDXyw7QM+2/MZAGHFw1jRaQVVi1V1Y6cLJncUMfbkOXeppVByP5jSealRq8CiWDOI1uBOFlQIIVxLgjuRnqJY69b9OArMeggsBZ0WQrmnHW7i9O3TdF3dlQOXDgDwZr03+bzl5/h6+7qp0wWbWzJ3Hrxa1raYIS/m3KlUKvy1XsTrTfYMor3OnWTuhBAuIsGdSJUSBxuGwpHV1uOKz1p3m/Av4nATkUcieX3D68Tp4wj2CWbRi4t4qepL7umvANw7LOuZde6s7ykvVsuCtQRLvN5kzyDaMnc6WVAhhHARCe6E1ZU/rMOwt06DSgMtIiD8LYeHYRMNibz949ssOrwIgIZlGrK843LKFirrxk4LSFvnznXBgafOuTNbFPt78suDBRVgWzGrt9e6S5EFFUIIF5PgrqBTFPh1AWx5D8wGKFQGOi2CMvUdbuLPq3/SeVVnjt84jgoVHzT+gLFNxuKllr9eucE2586VRYx9tJ4Z3KWtL5cXCyogtXiybX/Z1O3HJHMnhHAN+e1bkCXHwvq34Nh663Hl56HdTPALcehyRVGY9ess3vnpHfRmPaUCS/Hfl/5LswrN3NdncR+3rpY1WFzWZn5gy5apVa4Nhp1hm+tnm89oq1Moc+6EEK4iwV1BdekgrOwDsedA7Q3Pjoen3gCVyqHLbyXfot/6fqw7vg6ANpXasLjdYor5O14mRbiGO4sYe9qcuwT7SlkvVA7+XXc1/3v2l7WXQpFhWSGEi0hwV9AoCuybDVFjwWKE4HLw8mIoXdfhJnaf382rq1/lQtwFvNXeTHl2CkMaDMmzX5YFnd6dq2WNZhRF8Zh7m3R3MYV/HqyUtfHTpd9f1r5DRR5lEoUQnkeCu4Ik6RZ8PwhO3N0VouqL8OIM8A126HKzxcynuz5l3I5xWBQLFUMqEtkxkrqlHA8Mheu5c29Zs0XBaFbQenlGcGeb5+aXBzXubOyZO5lzJ4RwEwnuCooLB2BVX7hzATRaaPUpPNnf4WHYS3GX6L62O9FnowHo/kR3Zj0/i0BdoBs7LRzhjiLGvmkCjWSj2aWBY16yDYXmVRkUSJ1zZ8sipq6WleBOCOEaEtx5OosF9s6ArePBYoLCFeDlJVCqlsNNbPhnA73X9eZm8k38vf2Z1WYWPWv2dFuXhXMMbthb1lujQqNWYbYopBjNFPL1dlnbeclWODivyqCkfe3UzJ3sUCGEcC0J7jxZ4k1YNxD+/cl6XL0DvPAl+AQ5dLnepGfUz6P4cv+XANQOrU1kp0geL/K4u3ossiG1zp3rggOVSoWft7XYriftUpFkW1CRh3PubCVY7Jk7+4IKydx5EovFQmxsrEPnBgcHu7UvouCR4M5TndsDq/pB/GXQ6KD1ZKjb2+Fh2H9u/kOXVV04FHMIgCENhjC5xWR0Xjo3dlpkh31Y1sVDpz5aa3CX5EHBnS1zl1c17uD+zJ2UQvFMsbGxTNtwCB//zKeupCTGM7xt7VzqlSgoJLjzNBYL7J4G2z8FxQxFKlmHYUNrONzEt398yxsb3yDRmEgR3yIsab+Eto+3dV+fRY64I3MHnrlLRaK9FEpeLqhIv1o2dUGFDMt6Gh//QPyDgvO6G6IAkuDOkyRchzUD4PR26/ETnaHNNNAFOHR5vD6eQZsG8e2f3wLQpFwTlnVYRumg0u7qsXABd5RCAc+sdWdfLZunpVDurXPn+u3jhBAFmwR3nuLMTljdHxKugpcvtPkcanVzeBj29yu/02VVF/699S9qlZqIJhG83+h9NGr5hZPf2UuhaFx7r+xbkHnQsKy9zl0eDsvem7nTS+ZOFGDOzE20WDxrxxx3kuDuYWcxw87PYMdkUCxQrIp1GLZ4VYcuVxSFr/Z/xYioERgtRh4JeoTlHZbTqFwj9/ZbuIw76twB+N4NNjxyWDY/rZaVUiiiAHNmbmLHMMe2xhQS3D3c4mOsw7BndlqPa3WH56eA1t+hy28k3aDP933Y8M8GANpVbseidosI8ZUfoIeJu4dlPSq4sxcxzh+rZW1FokGCO1FwydxE15Pg7mF1ahuseQ0Sr4O3P7SdBjW7OHx59Nlouq3pxuX4y+g0Oj5v+TmDnhzkMdtMFSTuKGIMqVuQedKcO9tQaEAeLqhIm7lL+9nKsKwQwlUkuHvYmE0QPRF2TQUUKF7dOgxbzLHacyaLifE7xvPxzo9RUKhcpDIrOq2gZmhNt3ZbuI99tayLgwNbJsmT5twl6PN+QYV/mr1l0wV3sqBCCOEiEtw9TO5csi6aOL/Hely3Dzw3Ebx9Hbr8wp0LvLrmVXaf3w1A31p9+ar1V/g7OIwr8qfUBRUyLJsV+4KKvFwtq03dtzc+xRpsajVq1GrJmgshXEOCu4fFv1HWYdjkW6ANhBemQ1gnhy9fd3wdfb/vy+2U2wRqA5nbdi5dw7q6r78i19iGZaXOXdZsc+7yss5d2qzhzUQD4Pp7J4Qo2CS4y+/MRtg2AX6xbgFG6BPWYdgijzl0eYophXe2vMOs32YB8GSpJ/mu43c8FuLY9SJ/M5ktmC3WCfkuX1Bhm3PnQcOyqUWM8+6rT6NW4eOtJsVo4ZYtuJPFFEIIF5LgLj+LvQCr+sLFA9bjJwdAy4/B28ehy49dP0aX1V348+qfALwb/i6fPPMJWo3WXT0WucyWtQM3bD/mkZk763vxy8NSKNbX9yLFaOBWoh6QxRRCCNeS4C6/Or4J1r0BKbGgKwTtZkC1dg5dqigKiw8v5q0f3yLJmEQxv2J889I3PFfxOff2WWTJbDZjNBpd1l5CsoHSgdZAxWI0kGIxZbsto9GIl5cXKSkpmM1mgrVQOlCDFjMpKSmu6nKeMZotFPNVARq0mPL0PZUP9sZXbSYxOZnSgRrKBHml68+99yK/0Wg0eHl5yep6IfIpCe7yG5MBfh4H+2Zaj0vVgU6LIKSCQ5ffSbnDwI0DiTwSCUCLR1vwTftvKBlY0k0dFo5KSEjg4sWLKIrisjbNFoVxzYqjAi6cP5ejthRFITQ0lAsXLqBSqajsb2Jcs+L4eqs5c+aMazqchyx3PyuAG1cucjMPA5O36xfCaA4i0EfPuGbF0WpU6T7je+9FfuTn50fJkiXRamUkQIj8RoK7/OT2Wesw7KWD1uOn3oQWH4GXY1+eBy4doOvqrpy+fRqNSsPHzT9mZMORqFUy5JPXzGYzFy9exM/Pj2LFirnsF7bBZMZ8IxGVSkWFEplXeM+KxWIhISGBgIAA1Go1d5INeN9JwU+roUzIw7+i2mCyYLqRgEql4tEcflY5pb6ZSIrRTJCPN74pRny9NZQtkvoZ33sv8hNFUTAYDFy/fp0zZ85QqVKlfNdHIQo6Ce7yi6Pr4fvBoL8DPsHQfjZUed6hSy2Khal7pvLetvcwWUyUK1SO7zp+R3iZcPf2WTjMaDSiKArFihXD19ex0jWONWxG5WW0TtL3cWwu5oNYLBYMBgM+Pj6o1Wr0igZVogWVl1eO284XjGZUXgaXfFY55a01oVdM4OWNykuFlzb9Z3zvvchvfH198fb25ty5c/Z+CiHyDwnu8ppJDz99AAfmWY8fedI6DBtc1qHLryZcpde6Xmw5tQWAl6u9zLwX5hHsE+ymDouccPUQm22E1x1Dd7aya64cRs5LlrvvQ5MPhjnVd/tgursRujof9MlZ+THoFEJYSXCXl26eglV94Mof1uOGQ6D5h6Dxdujyn0//TPc13bmaeBUfLx++eu4r+tfpn2/n6AjXU7AGLO74NWsLOCyeEdthuftG8kMgpbkbOZvv7iubD7okhPAgEtzllSOrYf0QMMSDbwi8NBceb+nQpUazkbHbxzL5l8koKFQvVp0VnVZQvXh1N3da5De5kbmzeEzmzvrf/LAThK0LpnwUcAohPIcEd7nNmAybx8DBxdbjsuHQcSEUKu3Q5Wdjz9J1dVf2XdwHwMC6A5nWahq+Dm5BJvIPi8VCbGxsjtqITzFy53YSOm81t7RZl1gJDg52eDhNZc/c5V1wd/bsWSpUqMChQ4eoVatWjtoyK7ZAKmd9On78OL179+bw4cNUqVKFw4cPO92GLcC0fbaOxnbjxo1j3bp12XpNIUTBIcFdbrrxL6zsDVePACpoNByavgcax27Dyr9XMuCHAdzR36GQrhALXlxAp2qOb0Em8pfY2FimbTiEj3/2V24azGbuJJnw1qgI9st8VXVKYjzD29YmJCTEobbVKhUfDnuT9au+Y+LEiYwePdr+3Lp163jppZceqvl4rhqWjYiIwN/fnxMnThAQEJCtNu7tg6N9evfdd3nrrbey9ZpCiIJDgrvc8scK2DAMjIngVxQ6zIOKzzh0aZIxiWGbhzHvd+uii/BHwlnecTnlg8u7scMiN/j4B+IfFJzt671MZgwaI1qNGn9/19Ybs2W4dDofJk+ezOuvv07hwoVd+hq5yTYsq1GrMBgM2a7PdurUKdq0aUO5cuUcvube17s/uHOsnYCAgGwHlCL7LBYLt27dwts76/nQzmTHhXAX+RvoboYk+H4QrH3NGtiVbwRv/OJwYHfk2hHqz6/PvN/noULFe/95jx29d0hgJ4DUOXe4YcqWLQBp0KgJoaGhTJw48YHnjhs37r5h0+nTp1O+fHn7ce/evWnfvj2ffvopJUqUIDg4mPHjx2MymRgxYgQhISE88sgjLF68+L72jx8/ztNPP42Pjw81atRgx44d6Z4/cuQIrVu3JiAggBIlStCjRw9u3Lhhf75p06aMemcIU8aNoWalsrRq1SrD92GxWBg/fjyPPPIIOp2OWrVqsXnzZvvzKpWKgwcPMn78eFQqFePGjcuwnaZNmzJ48GCGDh1K0aJF7a9n62eFkkVoVvtx3hvyOrdv3USlUjFv3jxKlSqFxWJJ11a7du3o27fvAz/nBQsWULVqVXx8fKhSpQqzZs2yP9epUycGDx5sPx46dCgqlYrjx48D1qDT39+fn3/+GYBVq1YRFhaGr68vRYoUoUWLFiQmJmb4HguSxMREvvzxD2ZtP5npn2kbDuV4qoUQriDBnTtdOw7zm8Oh/wIqaDIaen4PgaFZXqooCnN/m8uT85/k7+t/ExoQSlSPKD555hO8HVxNKzyfG2M7+zwwjVrD+I8/ZsaMGVy8eDFHbW7bto3Lly+zc+dOpk2bRkREBG3btqVw4cLs37+fgQMH8vrrr9/3OiNGjOCdd97h0KFDhIeH88ILL3Dz5k3AOrzdvHlzateuzW+//cbmzZu5evUqr7zySro2/vfdMry9vfl+y1bmzJmTYf++/PJLpk6dyueff86ff/5Jq1atePHFF/n3338BuHLlCtWrV+edd97hypUrvPvuuw98r0uXLkWr1fLLL78wZ86cdP3cumsPs75dxc3r1xnxRh/UKnj55Ze5efMm27dvt7dx69YtNm/eTLdu3TJ8jWXLljF27Fg++eQTjh07xqeffsqHH37I0qVLAWjSpAnR0dH283fs2EHRokXtj/36668YjUaefvpprly5QteuXenbty/Hjh0jOjqaDh06PFRD7+7kG2DNsmf2JydTLIRwJQnu3EFRrAHdvKZw/RgElLAGdc3GgDrrDctjU2J5ZdUrDNw4kBRTCs9VfI4/Bv7BM486lu0TBYiTE/KdoVKp7FFju3btqVWrFhERETlqMyQkhK+++orKlSvTt29fKleuTFJSEu+99x6VKlVizJgxaLVadu/ene66wYMH07FjR6pWrcrs2bMpVKgQCxcuBODrr7+mdu3afPrpp1SpUoXatWuzaNEitm/fzj///GNvo8KjjzHs/fE8/nhlKleunGH/Pv/8c0aNGkWXLl2oXLkykydPplatWkyfPh2A0NBQvLy8CAgIIDQ0NNMh0kqVKjFlyhQqV7a+Xvp+VqVqjScYP3UGv+7ZxemTJylcuDCtW7dm+fLl9jZWrVpF0aJFadasWYavERERwdSpU+nQoQMVKlSgQ4cODBs2jLlz5wLWDOLRo0e5fv06t2/f5ujRowwZMsQe3EVHR/Pkk0/i5+fHlStXMJlMdOjQgfLlyxMWFsabb74pw8BCPIRkzp2r6RNg4zvwp3VvVx5tCh3mQ0Bxhy7fe2EvXVd35dydc3ipvZj0zCSGhQ+TLcREhlJzKu4ppWFr1aLA5MmTad68eabZqqxUr1493XykEiVKUKNGDfuxRqOhSJEiXLt2Ld114eGpu614eXlRr149jh07BsAff/zB9u3bMwxCTp06xeOPPw5AjZq1gQcvXoiLi+Py5cs0bNgw3eMNGzbkjz/+cOZtAlC3bt10x/f2M239wHNnT9Ogdg26devGgAED+PrrrwH47rvv6NKlS4ZzuBITEzl16hT9+vVjwIAB9sdNJhOFChUCoEaNGoSEhLBjxw60Wi21a9embdu2zJxp3bt6x44dNG3aFICaNWvyzDPPEBYWRqtWrWjZsiWdOnV6qOdZClFQSXDnSjFHrEWJb/wDKjU0ew/+8w44MLnWoliYvHsyH27/ELNi5tHCjxLZMZInSz+ZCx0XDyt3DstC+nIojRs3plWrVowZM4bevXunO0+tVt83fGc03l+a5d4J6SqVKsPH7p13lpmEhAReeOEFJk+efN9zJUuWtP+/r6+fta+5VFLO3z/9frxp+5lkMHH+ZpL9udpVKgDwwgsvoCgKGzdupEqVKuzatYsvvvgiw/YTEhIAmD9/Pg0aNEj3nEZjHSFQqVQ0btyY6OhodDodTZs25YknnkCv13PkyBH27NljD9Y1Gg1RUVHs2bOHn376iRkzZvD++++zf/9+KlSo4JoPRQiRKyS4cwVFgYNLYPNoMKVAYElr7bryDbO8FOBK/BV6rO3B1jNbAehaoytz2s4hSBfkxk4Lj+Du6O4uW5Zp0qRJ1KpV675hzWLFihETE4OiKPaA0JW12Pbt20fjxo0Ba2bq4MGD9oUCderUYfXq1ZQvXx4vrwd/pdk+Ks0DorugoCBKlSrFL7/8QpMmTeyP//LLL9SvXz/H7yFtP02KCoLi7c8FBFgDQR8fHzp06MDy5cupWbMmlStXpk6dOhm2V6JECUqVKsXp06cfOCcPrPPu5s+fj06n45NPPkGtVtO4cWM+++wz9Hp9ukylSqWiYcOGNGzYkLFjx1KuXDnWrl3L8OHDc/z+hRC5R4K7nEqJgw1DrTtOAFR8Fl6aA/5FHbp888nN9Fzbk+tJ1/Hz9uPr1l/Tu1Zv2UKsgEhJjM/6pEwkGkwk682g1aAxZP7jnJ3Xsv0ttGXlwsLC6NatG1999VW685o2bcr169eZMmUKnTp1YvPmzfz4448EBbnmHygzZ86kUqVKVK1alS+++ILbt2/bV5AOGjSI+fPn07VrV0aOHElISAgnT54kMjKSBQsW2LNYipJ1nbsRI0YQERHBY489Rq1atVi8eDGHDx9m2bJlOX4Pafs5bPg73DbrOH/2NJvXr2Hp4oWANYPZrVs32rZty5EjR+jRo0embX700Ue8/fbbFCpUiOeeew69Xs9vv/3G7du37QFZ06ZNGTZsGFqtlv/85z/2x959912efPJJe4Zx//79bN26lZYtW1K8eHH279/P9evXqVq1ao7fuxAid0lwlxNX/rAWJb51GlQaeGYsPP22Q8OwBrOB97e+z+d7PwfgiRJPsKLTCqoUreLmTov8Ijg4mOFta+eojZi4ZG7GGygSqCU0KOtdSoKDg51q3xYHpd2lYvz48axYsSLdeVWrVmXWrFl8+umnTJgwgY4dO/Luu+8yb948p17vQSZNmsSkSZM4fPgwFStWZP369RQtav0HlC3bNmrUKFq2bIler6dcuXI899xz6eaq2d5CZsOyb7/9Nnfu3OGdd97h2rVrVKtWjfXr11OpUqUcv4e0/WzzfGtSUvSUfKQMDZs8Yw9AAZo3b05ISAj//vsvXbt2zbTN/v374+fnx2effcaIESPw9/cnLCyMoUOH2s8JCwsjODiYxx9/3D7fr2nTppjNZvt8O7BmLnfu3Mn06dOJi4ujXLlyTJ06ldatW+f4vQshcpcEd9mhKPDrAtjyHpgNEPQIdFoEZRtkfS1w6tYpuq7uyq+XfwVg8JOD+azlZ/h4+biz1yKfUavVDu8W8SAp6mRMXnpCAn0IKeT6vz+fzZhLgt6UbvJ/+fLl0ev19507cOBABg4cmO6x9957z/7/S5Ysue+atGU6bM6ePZvutWwZt8wCnUqVKrFmzZoHPh8dHc2xK3EYzZZM95ZVq9VERERkuirYkeHmjN5X2n5aLApHLt+xP552qFitVnPx4kXi4uLuy3yOGzfuvtp6r776Kq+++uoD+6JWq7l161a6x2rVqnXfHMmqVaumq+knhHh4SXDnrJQ7sP4tOPq99fjx1tB+Fvg59kv6u7++4/UNrxNviKewT2EWtVtE+yrt3ddf4dGc3ZvUWbYhTIvl4a91ZnFgWDa3WKvMqFCw3b+875MQwnNIcOeMSwdhZR+IPQdqb3j2I3jqTYd+syYaEnn7x7dZdHgRAI3KNmJZh2WUKVTG3b0WHsyRocacsAd3D3lspygKtgW4+SO4U6FWg9nWp7ztjhDCw0hw5whFgf1z4KcPwWKE4LLQaQk8UjfLSwH+iPmDLqu7cPzGcVSo+LDxh3zY5EO81PLxi5yxBXfuyvzYgsaHfZcCBexZsvyy7adapcIsmTshhBtIdJGVpFvw/WA4sdF6XPUFePFr8A3O8lJFUZj16yze+ekd9GY9pQJLsazDMpqWb+rWLouCwz4s66b2bfPTHvbMXdphZU0+CaTSZhBzq/aeEKJgkOAuMxd+tRYlvnMBNFpo+QnUH+DQMOyt5Fv0W9+PdcfXAdD28bYsbreYon6OlUgRnsnVGTB7mTs3BSwZrZZ9GKWdb5dfsmRpA7r8MFTsrIc9myuEJ5PgLiMWC+ydAVvHg8UEhSvAy0ugVC2HLt91bhfd1nTjQtwFtBotnz37GW/Vfyvf/FIRuc9W6sJgMODrm3XJEkel1m5zWZPppM65e7h/kVvscxPzz89g2lW7+ahbDktKsu6wce8OI3nBYrEQGxub5XnBwcEZbuUmhKeR4O5eiTdh3Rvw7xbrcfUO8MKX4JN1MVazxcwnuz7hox0fYVEsVAqpRGSnSOqUzLjCvCg4vLy88PPz4/r163h7e7vsF4zJoEcxmTEa1KSoHd+yKyMWiwWDwUBKSoq9f2ajHsVkwKhXSEl5eH8pJhlMKCYDaNSkpKTkdXesTAYUkwmVSnVfaZmM7kV+oSgKSUlJXLt2jeDg4HQ1+vJKbGws0zYcwsc/8IHnpCTGM7xt7RyXHxLiYSDBXVrn9sKqvhB/GTQ6aD0J6vZx6J/Vl+Iu0W1NN3ac2wFAz5o9+br11wTqHvxlIwoOlUpFyZIlOXPmDOfOnXNZu9fiUzCYFCxxWny8c/ZLVlEUkpOT8fX1tWeZE/QmYpOMxHtrSLmtdUWX80SK0cyNBAPeGhWqhPxRT/JWooEkgxm1CryT0mdzM7oX+U1wcDChoaEub9fRLJytD7bg18c/EP+gYJe/xr17BAvxMJDgDqzDsL98Ads+AcUMRSpah2FDwxy6fMM/G+i9rjc3k28SoA1gdpvZdH+iu3v7LB46Wq2WSpUqYTAYXNbmJ0t/5cyNRD7rVJOq5QrnqC2j0cjOnTtp3Lixfahty98xTNl+nCfLhzCpY+UsWsi/dv1znXHb/6ZaySBmvJo/ttNaveUEPx65RmF/LasGVkv3XEb3Ij/x9vZ2W8bOkSwc5CwT58xrvNWqhtPtC5HXJLhLuA5rX4NT26zHT3SGNtNAF5DlpXqTnlE/j+LL/V8CUKdkHSI7RlKpSM63KhKeSa1W4+PjuszR5Xgzl+LNeOt0OW5Xo9FgMpnw8fGxBxRe3jouxZspnWB2ab9zW7xJxaV4M4+GavLN+zCrvbgUb0btzX19yuheFCTOZOHy82sIkVfyxWSOmTNnUr58eXx8fGjQoAEHDhzI9PyVK1dSpUoVfHx8CAsLY9OmTdl74TO7YM5/rIGdl6+1xMlLcx0K7P65+Q/hC8Ptgd3QBkPZ03ePBHYiV+lN1nl2Wo17fpR9tdZ2U4xmt7SfWxL1JgD8tfnn37N+Wmvmy8cr7+esCeHJnI0xPEGeB3crVqxg+PDhRERE8Pvvv1OzZk1atWrFtWvXMjx/z549dO3alX79+nHo0CHat29P+/btOXLkiFOvq9o/G755ERJioGhleG071Onh0Py6b/74hjpz63Ao5hBF/YqyoesGvnjuC3ReOqf6IERO2YM7L/f8KNvm8SUbHvLg7m7//XT5J5Dyuxto5nSupBDiwZyNMTxFnv8zdtq0aQwYMIA+ffoAMGfOHDZu3MiiRYsYPXr0fed/+eWXPPfcc4wYMQKACRMmEBUVxddff82cOXMcfl3N/lkQpIZa3eH5KRy9Yeb8kSuZXpNsTGHe7/PuLpp4gieL1WBIgyFoDCFszuJacT+TycwfN1Vo/r6Kl2QvsiXJYM1IuSu4870beNxOMjzUf8f/unQHgABdnn/l2fnbMnfeefdvbGcXLwBOL3bIzmsI4SrOxhieIk+/6QwGAwcPHmTMmDH2x9RqNS1atGDv3r0ZXrN3716GDx+e7rFWrVqxbt26DM/X6/XpygzcuWP9kr+c5MWNp0ahlGsFf//D3F1n+P6PmPuu1+hSV0qZ9YlAGQphXSxx4RK8e3jHA6+xnp+1+1/D8Wvc9Rq52qffthbM9+3C1zh9/AhxF1Mzx4ULpy6uuH37dpbtFy5cGKPRyI0bNzh8+DBeXtavhnO3ktBfP8el69Dni3+d6tOD3kN2rnHVa8RdMnLokNF+7OznlPYaZ8+/95rr566jv36OJJ9YDh1Kv1o2ICDgvnuRndfI6vzbt28ze8shtD5+mZ5vSEnijVa1AZw6v3Dhwtl6jasXz+Ljl/n0mJSkBM6d8yYuLo7bt29neY2z59uuOX9exa1bt7hi8sI/MPPPNruvce6cdV5lfnrf7uxTdl/jks76d+jOnTsEBaWWJ9PpdOh094+cZSfG8BhKHrp06ZICKHv27En3+IgRI5T69etneI23t7eyfPnydI/NnDlTKV68eIbnR0REWLeVlD/yR/7IH/kjf+SPx/2JiIhwWYzhKfLPGIWbjBkzJl2m79atW1SoUIEjR45QqFChPOyZiI+Pp1q1ahw9epTAQKkHmNfkfuQfci/yD7kX+cedO3eoUaMGZ86cSVcCJ6OsXUGXp8Fd0aJF0Wg0XL16Nd3jV69efWBxzNDQUKfOf1C6tkyZMunSuiL3xcXFAVC6dGm5F/mA3I/8Q+5F/iH3Iv+wff4hISEO3YvsxBieIk9Xy2q1WurWrcvWrVvtj1ksFrZu3Up4eHiG14SHh6c7HyAqKuqB5wshhBCi4MlOjOEp8nxYdvjw4fTq1Yt69epRv359pk+fTmJion1lS8+ePSldujQTJ04EYMiQITRp0oSpU6fSpk0bIiMj+e2335g3b15evg0hhBBC5DNZxRieKs+Du86dO3P9+nXGjh1LTEwMtWrVYvPmzZQoUQKA8+fPp9s4++mnn2b58uV88MEHvPfee1SqVIl169ZRo4ZjW8TodDoiIiJkjD4fkHuRv8j9yD/kXuQfci/yj+zci6xiDE+lUhRFyetOCCGEEEII18jzHSqEEEIIIYTrSHAnhBBCCOFBJLgTQgghhPAgEtwJIYQQQngQjwzuZs6cSfny5fHx8aFBgwYcOHAg0/NXrlxJlSpV8PHxISwsjE2bNuVSTz2fM/di/vz5NGrUiMKFC1O4cGFatGiR5b0TznH2Z8MmMjISlUpF+/bt3dvBAsTZexEbG8ugQYMoWbIkOp2Oxx9/XL6rXMTZezF9+nQqV66Mr68vZcqUYdiwYaSkpORSbz3Xzp07eeGFFyhVqhQqleqBe8anFR0dTZ06ddDpdFSsWJElS5a4vZ8Phbze/8zVIiMjFa1WqyxatEj5+++/lQEDBijBwcHK1atXMzz/l19+UTQajTJlyhTl6NGjygcffKB4e3srf/31Vy733PM4ey9effVVZebMmcqhQ4eUY8eOKb1791YKFSqkXLx4MZd77pmcvR82Z86cUUqXLq00atRIadeuXe501sM5ey/0er1Sr1495fnnn1d2796tnDlzRomOjlYOHz6cyz33PM7ei2XLlik6nU5ZtmyZcubMGWXLli1KyZIllWHDhuVyzz3Ppk2blPfff19Zs2aNAihr167N9PzTp08rfn5+yvDhw5WjR48qM2bMUDQajbJ58+bc6XA+5nHBXf369ZVBgwbZj81ms1KqVCll4sSJGZ7/yiuvKG3atEn3WIMGDZTXX3/drf0sCJy9F/cymUxKYGCgsnTpUnd1sUDJzv0wmUzK008/rSxYsEDp1auXBHcu4uy9mD17tvLoo48qBoMht7pYYDh7LwYNGqQ0b9483WPDhw9XGjZs6NZ+FjSOBHcjR45Uqlevnu6xzp07K61atXJjzx4OHjUsazAYOHjwIC1atLA/plaradGiBXv37s3wmr1796Y7H6BVq1YPPF84Jjv34l5JSUkYjcZ0G0SL7Mnu/Rg/fjzFixenX79+udHNAiE792L9+vWEh4czaNAgSpQoQY0aNfj0008xm8251W2PlJ178fTTT3Pw4EH70O3p06fZtGkTzz//fK70WaSS398Pluc7VLjSjRs3MJvN91WeLlGiBMePH8/wmpiYmAzPj4mJcVs/C4Ls3It7jRo1ilKlSt33wyucl537sXv3bhYuXMjhw4dzoYcFR3buxenTp9m2bRvdunVj06ZNnDx5kjfffBOj0UhERERudNsjZedevPrqq9y4cYP//Oc/KIqCyWRi4MCBvPfee7nRZZHGg35/x8XFkZycjK+vbx71LO95VOZOeI5JkyYRGRnJ2rVr8fHxyevuFDjx8fH06NGD+fPnU7Ro0bzuToFnsVgoXrw48+bNo27dunTu3Jn333+fOXPm5HXXCpzo6Gg+/fRTZs2axe+//86aNWvYuHEjEyZMyOuuCWHnUZm7okWLotFouHr1arrHr169SmhoaIbXhIaGOnW+cEx27oXN559/zqRJk/j555954okn3NnNAsPZ+3Hq1CnOnj3LCy+8YH/MYrEA4OXlxYkTJ3jsscfc22kPlZ2fjZIlS+Lt7Y1Go7E/VrVqVWJiYjAYDGi1Wrf22VNl5158+OGH9OjRg/79+wMQFhZGYmIir732Gu+//366vdCFez3o93dQUFCBztqBh2XutFotdevWZevWrfbHLBYLW7duJTw8PMNrwsPD050PEBUV9cDzhWOycy8ApkyZwoQJE9i8eTP16tXLja4WCM7ejypVqvDXX39x+PBh+58XX3yRZs2acfjwYcqUKZOb3fco2fnZaNiwISdPnrQH2AD//PMPJUuWlMAuB7JzL5KSku4L4GxBtyJbtecq+f2dibxe0eFqkZGRik6nU5YsWaIcPXpUee2115Tg4GAlJiZGURRF6dGjhzJ69Gj7+b/88ovi5eWlfP7558qxY8eUiIgIKYXiIs7ei0mTJilarVZZtWqVcuXKFfuf+Pj4vHoLHsXZ+3EvWS3rOs7ei/PnzyuBgYHK4MGDlRMnTigbNmxQihcvrnz88cd59RY8hrP3IiIiQgkMDFS+++475fTp08pPP/2kPPbYY8orr7ySV2/BY8THxyuHDh1SDh06pADKtGnTlEOHDinnzp1TFEVRRo8erfTo0cN+vq0UyogRI5Rjx44pM2fOlFIod3lccKcoijJjxgylbNmyilarVerXr6/s27fP/lyTJk2UXr16pTv/f//7n/L4448rWq1WqV69urJx48Zc7rHncuZelCtXTgHu+xMREZH7HfdQzv5spCXBnWs5ey/27NmjNGjQQNHpdMqjjz6qfPLJJ4rJZMrlXnsmZ+6F0WhUxo0bpzz22GOKj4+PUqZMGeXNN99Ubt++nfsd9zDbt2/P8HeA7fPv1auX0qRJk/uuqVWrlqLVapVHH31UWbx4ca73Oz9SKYrkkYUQQgghPIVHzbkTQgghhCjoJLgTQgghhPAgEtwJIYQQQngQCe6EEEIIITyIBHdCCCGEEB5EgjshhBBCCA8iwZ0QQgghhAeR4E4Ike/17t2b9u3b24+bNm3K0KFDc70f0dHRqFQqYmNjc/21hRDCURLcCSGypXfv3qhUKlQqFVqtlooVKzJ+/HhMJpPbX3vNmjVMmDDBoXNzOyArX768/XPx8/MjLCyMBQsW5MprCyEESHAnhMiB5557jitXrvDvv//yzjvvMG7cOD777LMMzzUYDC573ZCQEAIDA13WnquNHz+eK1eucOTIEbp3786AAQP48ccf87pbQogCQoI7IUS26XQ6QkNDKVeuHG+88QYtWrRg/fr1QOpQ6ieffEKpUqWoXLkyABcuXOCVV14hODiYkJAQ2rVrx9mzZ+1tms1mhg8fTnBwMEWKFGHkyJHcu0vivcOyer2eUaNGUaZMGXQ6HRUrVmThwoWcPXuWZs2aAVC4cGFUKhW9e/cGwGKxMHHiRCpUqICvry81a9Zk1apV6V5n06ZNPP744/j6+tKsWbN0/cxMYGAgoaGhPProo4waNYqQkBCioqKc+GSFECL7JLgTQriMr69vugzd1q1bOXHiBFFRUWzYsAGj0UirVq0IDAxk165d/PLLLwQEBPDcc8/Zr5s6dSpLlixh0aJF7N69m1u3brF27dpMX7dnz5589913fPXVVxw7doy5c+cSEBBAmTJlWL16NQAnTpzgypUrfPnllwBMnDiRb775hjlz5vD3338zbNgwunfvzo4dOwBrENqhQwdeeOEFDh8+TP/+/Rk9erRTn4fFYmH16tXcvn0brVbr1LVCCJFtihBCZEOvXr2Udu3aKYqiKBaLRYmKilJ0Op3y7rvv2p8vUaKEotfr7dd8++23SuXKlRWLxWJ/TK/XK76+vsqWLVsURVGUkiVLKlOmTLE/bzQalUceecT+WoqiKE2aNFGGDBmiKIqinDhxQgGUqKioDPu5fft2BVBu375tfywlJUXx8/NT9uzZk+7cfv36KV27dlUURVHGjBmjVKtWLd3zo0aNuq+te5UrV07RarWKv7+/4uXlpQBKSEiI8u+//z7wGiGEcCWvvA0thRAPsw0bNhAQEIDRaMRisfDqq68ybtw4+/NhYWHpMlZ//PEHJ0+evG++XEpKCqdOneLOnTtcuXKFBg0a2J/z8vKiXr169w3N2hw+fBiNRkOTJk0c7vfJkydJSkri2WefTfe4wWCgdu3aABw7dixdPwDCw8Mdan/EiBH07t2bK1euMGLECN58800qVqzocP+EECInJLgTQmRbs2bNmD17NlqtllKlSuHllf4rxd/fP91xQkICdevWZdmyZfe1VaxYsWz1wdfX1+lrEhISANi4cSOlS5dO95xOp8tWP9IqWrQoFStWpGLFiqxcuZKwsDDq1atHtWrVcty2EEJkRebcCSGyzd/fn4oVK1K2bNn7AruM1KlTh3///ZfixYvbgx/bn0KFClGoUCFKlizJ/v377deYTCYOHjz4wDbDwsKwWCz2uXL3smUOzWaz/bFq1aqh0+k4f/78ff0oU6YMAFWrVuXAgQPp2tq3b1+W7/FeZcqUoXPnzowZM8bpa4UQIjskuBNC5Jpu3bpRtGhR2rVrx65duzhz5gzR0dG8/fbbXLx4EYAhQ4YwadIk1q1bx/Hjx3nzzTczrVFXvnx5evXqRd++fVm3bp29zf/9738AlCtXDpVKxYYNG7h+/ToJCQkEBgby7rvvMmzYMJYuXcqpU6f4/fffmTFjBkuXLgVg4MCB/Pvvv4wYMYITJ06wfPlylixZkq33PWTIEH744Qd+++23bF0vhBDOkOBOCJFr/Pz82LlzJ2XLlqVDhw5UrVqVfv36kZKSQlBQEADvvPMOPXr0oFevXoSHhxMYGMhLL72UabuzZ8+mU6dOvPnmm1SpUoUBAwaQmJgIQOnSpfnoo48YPXo0JUqUYPDgwQBMmDCBDz/8kIkTJ1K1alWee+45Nm7cSIUKFQAoW7Ysq1evZt26ddSsWZM5c+bw6aefZut9V6tWjZYtWzJ27NhsXS+EEM5QKQ+apSyEEEIIIR46krkTQgghhPAgEtwJIYQQQngQCe6EEEIIITyIBHdCCCGEEB5EgjshhBBCCA8iwZ0QQgghhAeR4E4IIYQQwoNIcCeEEEII4UEkuBNCCCGE8CAS3AkhhBBCeBAJ7oQQQgghPIgEd0IIIYQQHuT//GB/cGEITd8AAAAASUVORK5CYII=",
      "text/plain": [
       "<Figure size 640x480 with 2 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 640x480 with 2 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 640x480 with 2 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "from sklearn.metrics import mean_squared_error, r2_score\n",
    "import statsmodels.api as sm\n",
    "\n",
    "\n",
    "# code from https://github.com/papousek/duolingo-halflife-regression/blob/master/evaluation.py\n",
    "def load_brier(predictions, real, bins=20):\n",
    "    counts = np.zeros(bins)\n",
    "    correct = np.zeros(bins)\n",
    "    prediction = np.zeros(bins)\n",
    "    for p, r in zip(predictions, real):\n",
    "        bin = min(int(p * bins), bins - 1)\n",
    "        counts[bin] += 1\n",
    "        correct[bin] += r\n",
    "        prediction[bin] += p\n",
    "    np.seterr(invalid='ignore')\n",
    "    prediction_means = prediction / counts\n",
    "    prediction_means[np.isnan(prediction_means)] = ((np.arange(bins) + 0.5) / bins)[np.isnan(prediction_means)]\n",
    "    correct_means = correct / counts\n",
    "    correct_means[np.isnan(correct_means)] = 0\n",
    "    size = len(predictions)\n",
    "    answer_mean = sum(correct) / size\n",
    "    return {\n",
    "        \"reliability\": sum(counts * (correct_means - prediction_means) ** 2) / size,\n",
    "        \"resolution\": sum(counts * (correct_means - answer_mean) ** 2) / size,\n",
    "        \"uncertainty\": answer_mean * (1 - answer_mean),\n",
    "        \"detail\": {\n",
    "            \"bin_count\": bins,\n",
    "            \"bin_counts\": list(counts),\n",
    "            \"bin_prediction_means\": list(prediction_means),\n",
    "            \"bin_correct_means\": list(correct_means),\n",
    "        }\n",
    "    }\n",
    "\n",
    "\n",
    "def plot_brier(predictions, real, bins=20):\n",
    "    brier = load_brier(predictions, real, bins=bins)\n",
    "    bin_prediction_means = brier['detail']['bin_prediction_means']\n",
    "    bin_correct_means = brier['detail']['bin_correct_means']\n",
    "    bin_counts = brier['detail']['bin_counts']\n",
    "    r2 = r2_score(bin_correct_means, bin_prediction_means, sample_weight=bin_counts)\n",
    "    rmse = mean_squared_error(bin_correct_means, bin_prediction_means, sample_weight=bin_counts, squared=False)\n",
    "    print(f\"R-squared: {r2:.4f}\")\n",
    "    print(f\"RMSE: {rmse:.4f}\")\n",
    "    plt.figure()\n",
    "    ax = plt.gca()\n",
    "    ax.set_xlim([0, 1])\n",
    "    ax.set_ylim([0, 1])\n",
    "    plt.grid(True)\n",
    "    fit_wls = sm.WLS(bin_correct_means, sm.add_constant(bin_prediction_means), weights=bin_counts).fit()\n",
    "    print(fit_wls.params)\n",
    "    y_regression = [fit_wls.params[0] + fit_wls.params[1]*x for x in bin_prediction_means]\n",
    "    plt.plot(bin_prediction_means, y_regression, label='Weighted Least Squares Regression', color=\"green\")\n",
    "    plt.plot(bin_prediction_means, bin_correct_means, label='Actual Calibration', color=\"#1f77b4\")\n",
    "    plt.plot((0, 1), (0, 1), label='Perfect Calibration', color=\"#ff7f0e\")\n",
    "    bin_count = brier['detail']['bin_count']\n",
    "    counts = np.array(bin_counts)\n",
    "    bins = (np.arange(bin_count) + 0.5) / bin_count\n",
    "    plt.legend(loc='upper center')\n",
    "    plt.xlabel('Predicted R')\n",
    "    plt.ylabel('Actual R')\n",
    "    plt.twinx()\n",
    "    plt.ylabel('Number of reviews')\n",
    "    plt.bar(bins, counts, width=(0.8 / bin_count), ec='k', lw=.2, alpha=0.5, label='Number of reviews')\n",
    "    plt.legend(loc='lower center')\n",
    "\n",
    "plot_brier(dataset['p'], dataset['y'], bins=40)\n",
    "for last_rating in (\"1\",\"2\",\"3\",\"4\"):\n",
    "    print(f\"Last rating: {last_rating}\")\n",
    "    plot_brier(dataset[dataset['r_history'].str.endswith(last_rating)]['p'], dataset[dataset['r_history'].str.endswith(last_rating)]['y'], bins=40)\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 640x480 with 2 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "import matplotlib.ticker as ticker\n",
    "\n",
    "def to_percent(temp, position):\n",
    "    return '%1.0f' % (100 * temp) + '%'\n",
    "\n",
    "fig = plt.figure(1)\n",
    "ax1 = fig.add_subplot(111)\n",
    "ax2 = ax1.twinx()\n",
    "lns = []\n",
    "\n",
    "stability_calibration = pd.DataFrame(columns=['stability', 'predicted_retention', 'actual_retention'])\n",
    "stability_calibration = dataset[['stability', 'p', 'y']].copy()\n",
    "stability_calibration['bin'] = stability_calibration['stability'].map(lambda x: math.pow(1.2, math.floor(math.log(x, 1.2))))\n",
    "stability_group = stability_calibration.groupby('bin').count()\n",
    "\n",
    "lns1 = ax1.bar(x=stability_group.index, height=stability_group['y'], width=stability_group.index / 5.5,\n",
    "                ec='k', lw=.2, label='Number of predictions', alpha=0.5)\n",
    "ax1.set_ylabel(\"Number of predictions\")\n",
    "ax1.set_xlabel(\"Stability (days)\")\n",
    "ax1.semilogx()\n",
    "lns.append(lns1)\n",
    "\n",
    "stability_group = stability_calibration.groupby(by='bin').agg('mean')\n",
    "lns2 = ax2.plot(stability_group['y'], label='Actual retention')\n",
    "lns3 = ax2.plot(stability_group['p'], label='Predicted retention')\n",
    "ax2.set_ylabel(\"Retention\")\n",
    "ax2.set_ylim(0, 1)\n",
    "lns.append(lns2[0])\n",
    "lns.append(lns3[0])\n",
    "\n",
    "labs = [l.get_label() for l in lns]\n",
    "ax2.legend(lns, labs, loc='lower right')\n",
    "plt.grid(linestyle='--')\n",
    "plt.gca().yaxis.set_major_formatter(ticker.FuncFormatter(to_percent))\n",
    "plt.gca().xaxis.set_major_formatter(ticker.FormatStrFormatter('%d'))\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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IiODgwYP4+/szYcIEXnzxRavtz58/nw8++IC4uDiaNm3KJ598wn333Xcrun77MRrgxJ+WUcejf4Axz1KuaKHuw5bZ1fW7ga7YnF4hyo1GoxRcwtGJB+tWt1pnNJmJS8vhv0uW4y1jk7L4ryDBjE3KIjkrn4sZeVzMyGNPbEqxtu11Gvw9nQjwfIgAz24EtHCilpuJ2qbT+GQdxy5xvyW5TDgMuanw39+WpZBWD9WDL0/e8WlsOfVQWc44IIQQt4lKkUT+888/fPbZZzRp0sSqfPTo0axevZqVK1fi7u7O8OHDeeKJJ/j7b8uXtdFopEePHvj4+LB9+3YuXLhAv379sLOz49133wUgJiaGHj16MHToUJYtW8aGDRsYNGgQvr6+hIWFAfD9998TERHBwoULad26NXPmzCEsLIyjR4/i5eV1a5+MyizugGVm9b7vLbv7Cnk3siSOTZ4GF3m+RMXTapTLV/KpXXx9ana+mlCqyyXL33MplktFnkjI4EQJV/EBf7zd6hDg2ZfAenqaOyRQnxj8c09QJe0IdokHUHLTCo673AfRRR5aJajIcZZNLX9dfWSkXghxW1LMZrO5IgPIyMigRYsWLFiwgOnTp9OsWTPmzJlDamoq1atXZ/ny5Tz55JMAHDlyhAYNGhAZGUmbNm34448/6NmzJ+fPn1dHJxcuXMgbb7xBYmIier2eN954g9WrV3PgwAF1m3369CElJYU1ayy7pFq3bs29997LvHnzADCZTPj7+zNixAjGjRtXpn6cPXsWf39/zpw5Q40aNcrzKapYGYmWY8L2LreMvBRyqmZJGps+a/lRFOIOUXipyP+SMoslmLGXskjPNVz18U56Dfe6Z9Da6RyNNP8RZDhJ9cxjOGRdKPkBztXBqwFUqWk5vtIjEKoU/HXxkgRTVIikpCQWbDqBs5tHRYeiykxL4ZUOdfD0vI7zAl/FHfv7fQtU+EhkeHg4PXr0oHPnzkyfPl0tj4qKIj8/n86dO6tlwcHBBAQEqElkZGQkjRs3ttq9HRYWxrBhwzh48CDNmzcnMjLSqo3COqNGjQIgLy+PqKgoxo8fr67XaDR07tyZyMjIUuPOzc0lNzdXvZ+enn7dz0GlY8iFY2sto47H11mu6AKgsYP6XS2n5an7sOUUN0LcYa52qUiz2UxKVr5l93hSwe7xS1n8l5TJmaRszqdmk5VnYkuiE1uoC9QFLN8/VUgjRBNLG8ezNNOfoa4pBq/cWDSZiRCTCDFbiwejcwSPAMtS5YoE0yMAHKtIkimEqDAVmkR+9913/Pvvv/zzzz/F1sXFxaHX6/Hw8LAq9/b2Ji4uTq1TNIEsXF+47mp10tLSyM7OJjk5GaPRWGKdI0eOlBr7jBkzmDJlStk6ejswm+H8v5YJMgd+sEyYKeTXwjJBplHv67syjBB3CEVRqOKsp4qznqb+HsXW5xqMnEvOthrBVJPNJC1/57nxd2Yj9So+9uQRrMRSWzmPv5JITW0itewuUYMEqhgvojFkX54lXhJ7t+KJZdHb9i4378kQQtz1KiyJPHPmDCNHjmT9+vU4ODhUVBjXbfz48URERKj3z507R0hISAVGdJ3SLliOcdz7LSQWSZpdfQt2Vz8HXsEVF58QtxF7nbbgnJXFkzez2czFjLyCBDOT2EuFyaY325OyiU/PwWwECuap2WHAV7mEv5KAv5KIv5JAgOYitXSXuIcEPEzJluvGx++3LCVxqlbKKGYgePjL5DchxA2psCQyKiqKhIQEWrRooZYZjUa2bt3KvHnzWLt2LXl5eaSkpFiNRsbHx+Pj4wOAj48Pu3btsmo3Pj5eXVf4t7CsaB03NzccHR3RarVotdoS6xS2URJ7e3vs7S9/AaelpdnQ+wqWnw1HVluuXX1qk+VycQA6B8tJwJs9C7U6yKXghChHiqJQ3dWe6q72tAysUmx9nsHEhdRsziVnczY5m7Mp2ZxNzuJccjZ/JWcTl5aD0WBWk0wHcqmhJFJDSSxIMhMJ0CQSpL3IPSTgYs6ArIuW5fy/JUVk+WexpFHMKoGWc2LKd4AQ4ioqLIns1KkT+/db//c8YMAAgoODeeONN/D398fOzo4NGzbQu3dvAI4ePUpsbCyhoaEAhIaG8s4775CQkKDOol6/fj1ubm7qqGBoaCi///671XbWr1+vtqHX62nZsiUbNmygV69egGVizYYNGxg+fPhN6/8tZzbDmZ0QvQwO/mIZwSjk38ayu7phL3Bwr6gIhbir6XUaAqs6E1jVucT1BqOJuLQcziZfTjTPpWRxNjmbrSnZnE/JJj/v8jxJV7LUUcwaSgI1lIsEKAnU1F3kHnMCDuRC+nnLElvC8d8anSWRtBrFrHk52XTxluMxhbjLVVgS6erqSqNGjazKnJ2dqVq1qlo+cOBAIiIi8PT0xM3NjREjRhAaGkqbNm0A6NKlCyEhIbzwwgvMnDmTuLg4JkyYQHh4uDpKOHToUObNm8fYsWN56aWX2LhxIytWrGD16tXqdiMiIujfvz+tWrXivvvuY86cOWRmZjJgwIBb9GzcApkX4avuWPaXAe4B0LSPZalawjlQhBCVik6rUc+LWRKjyUxieq5l9DKlYDQz2TKauSnFknjm5psgH8BMVdKsRjH9lQRqKIkEai/iRyJ2JgOk/GdZSgzIAarWgYA2ENjWsrh6l1xXCHFHqvDZ2Vcze/ZsNBoNvXv3tjrZeCGtVsuqVasYNmwYoaGhODs7079/f6ZOnarWCQoKYvXq1YwePZq5c+dSo0YNvvjiC/UckQDPPPMMiYmJTJw4kbi4OJo1a8aaNWuKTba5rblUt4w0au0to46BbUGjqeiohBDlRKu5fG3yViWsLzwms2iSea4gydxQcD8rzwj5oGDCm+SCUczLSaa/JpFATSJeJKE15ED8AcvyzxeWjVStA4H3Q+ADlr8e/rf0ORBC3FoVfp7IO4WcZ0oIcTsrPH1R0d3kZ9Xd5pZkMz3HcrovXcGknxDlP9poDtPR8TgB+TEoXPFz4hFwOaGs2dZysnXZBX5bkPNEirKo1CORQgghbo2ipy9qXKPkY6NTs/M5V5BUnknKYvOxRKYcu48p6eBGBg+7nOJZr7M0MR5An7AfUmIhZbnlYgUArn4FI5X3Q80HoFo9SSqFuI1JEimEEKJM3B3tcHe0I8TPcg3wlx4IIvZSFt/+E8vK3Wf4McOFHzOaoFG6062uC4NqJtLUeBBN7HY4F2WZxHPgB8sCllMQFSaUgfeDV0M5zEaI24gkkUIIIa5bQFUn3ugazOjO9Vh3KI7lO2PZfvISq49lsPqYI37uD9Dnvud4pldVvNMOwOm/4b+/4ew/ltMPHf7VsoDl7BABBbu+A++3XF9cKz9TQlRW8ukUQghxw/Q6DT2b+NGziR+nEjP4dlcsK6POcj41h1nrjzF3g0LnBl4813oQDz40Do0pD87vgdPbLEll7E7ISYVjf1gWAL0rBLS+PFnHrzno9BXbUSGESpJIIYQQ5apWdRfe6hHCa13qs+aAZXRy1+kk1h6MZ+3BePw9HXn2vgCeatmc6gFtgNfBaIALey0J5X9/w3+RkJsKJ/60LGC5lrj/vZdPKVSjFdg5VmhfhbibyezsciKzu4QQonTH4tNZvjOWH/89q87yttMqdAnxoW/rAEJrV0UpOsnGZIT4g0WSyu2Qdcm6Ua0e7mlZkFTeD/6t5Xrh5URmZ4uykCSynMibUAghri07z8iqfedZviuWPbEpanlQNWeeuy+A3i1r4Olcwi5rsxkSj8J/2ywJ5em/ISPOuo6iBb9ml0cqA9qAo8fN7M4dS5JIURaSRJYTeRMKIYRtDp1PY/mu//hlz3kyci2jk3qthm6NfejbOpB7a1axHp0symyGpFOWUcrTBSOVqbFXVFLAp7EloazZ1jJpx7nqze3UHUKSSFEWkkSWE3kTCiHE9cnMNfDr3vMs3xnL/nOpankdLxfL6GSLGrg72V27oZTYglHKgtHKpJPF61RvAL5NQe9kOcZSZ285rlJnX/p9nQPYOVj+Fi6F97X6O/Jcl5JEirKQiTVCCCEqlLO9jmfvC+DZ+wLYdzaF5Ttj+V/0eU4kZDB11SHeX3OEnk38eK51AC0CPEofnfQIsCxN+1jup124fDzlf39D4hFIPGxZyo1y9SSz2P2rJKvXuu9Y5bbYPZ9vNJGSlU9KVh7J2Za/bWtXw9leUo47jbyiQgghKo0mNTxoUsODN3s04H97zrFsZyxH4tL58d+z/PjvWYJ9XOnbOoBeze/B1eEao5NuvtD4ScsCkHnx8gilIRfysy1/Ddkl3zfkQH6O5W/hUnhfvcSjuaB+9s18Wi7zCLCMpPo2Bd9mlr8uXrdm20UYjCZSs/NJyc6/nDBm5ZOSnUdmrrFY/QY+bpJE3oHkFRVCCFHpuDnY8UJoTZ5vE8i/sZbRyVX7znMkLp23/3eQGX8c4dGmltHJJjU8ytaoczUIefTGgzObwZh39STzaslpme9f0W5+ZsGlJGPh8G+X43H1K5JYFixufje8m91kMpOclacmiSlZ+SRnW/4WzrAvjYNOg4eTHg8nOzyc7HB1kHTjTiTHRJYTOaZCCCFurtSsfH789yzLd8VyIiFDLW98jzvPtQ7g0aZ+d/ZoV3YKxO23nE/zwl64EA0Xj3N5VLQIp2qWZNKv2eXE0iOwWGJpNJk5n5JNzMVMTl/KJOaiZTkZn87ZlJySWlbptRo1SfRw1FPFyU5NHB3stOXX7wJyTGTlI0lkOZE3oRBC3Bpms5ldMUks3xXLH/vjyDOaAHCx19GruR/P3ReoXt/7jpebAfEHiiSWeyHhMJiL71I26N246NqAGLva7DcGsT3rHiJTPChh77NKp1HUJFFNGJ30eDja4aTXln586k0gSWTlI0lkOZE3oRBC3HpJmXn8EHWGb3edIeZiplrePMCD5+4LoGcTPxz15T8qVtmYzWYuZuRx+lImsfGXyDyzD138PjxSDxOQe5y6xGKvFN8FnWF24Ig5kP8c6pHiFoLJpwku94Tg6aCw+Ugc1at63tJE8Wokiax8JIksJ/ImFEKIimMymdlx6hLLdsay9mAcBpPlp83NQccTLWrQt3UAdb1dKzjKG5eSlXd513NiJjGXsjh9MZPTFzNJzy39OEUHjZEH3BK53/kcjTSnCco7jmfGMbTGnOKVdQ4YqgYTne9PcpUmJLgEc8mpFkZNxV63XJLIyucOPnhECCHE3UKjUbi/TjXur1ONhPQcVu4+y7e7YjmbnM2S7adZsv0099asQt/WgXRt5INGUTCYTOQbzRiMJgwmM/lGE0aT2VJmMmEwWsoMJjOGEsoK6xuMZvKvWGcsWF90naFIW2qZ+vdy/cK41DZMlscWzoQujaKAn7sjtao7U7OqMzWrORNUzYmgai7UqOKInVZj/QCjAS4dt94VfmEf5KWji4+mFdGQZJnAY1R0XHSqQ4JLfRKcg0lwCSbRqQ5GrcNNfFVFZScjkeVE/pMRQojKxWQys/V4Ist3xrLhSAJG053xc+fj5kDNak4EVbMki0HVLIu/p9ONT2gxmSDpFBkn/mbf7m3UyI/BK/MoDoa04lXRkuRUsyCpLEguneuRr3O+sRhKISORlY+MRAohhLgjaTQK7et70b6+F3GpOXz/zxm++yeWC6nFd+FqNQpajYKdRkGn1WCnVdBpNOi0CrqCMp1GwU5rKbPTaNBqFMvtK9ZZ2tFYrdNqi5fp1L+X1+m0GuwKY9EWbt8Sj7O9jsCqTjjpb+JPt0YD1eqQp/Fk47kGlivWmM245V7AK/MIXhmWxTvzCE75yVTLOkm1rJOEJK4GwIxCsmOAdWLpUp9c3V0y0ekuI0mkEEKIO56PuwMjO9dlRMc6pGTnFyRpBYmiRkGjqRyTRyolRSHNwY80Bz9OVO1oKTObcc5LxDvzCF4ZR9UE0zUvAc/s//DM/o/gi2vVJn4NnsnJqh0qqAPiZpEkUgghxF1Do1HwdK7YCSJ3BEUh096LU/ZenPJspxY75V2ieuZRvDKOFiSYR3DPPU+SY1AFBituFkkihRBCCFEusvRV+U9/P/9VuV8ts89PJVd3+8+MF8VJEimEEEKImybXzr2iQxA3iebaVYQQQgghhLAmI5FC3CFMJhMpKSkVHUYxHh4eaDTy/6oQQtxpJIkU4g6RkpLCrFV7cHCuPMce5WSmE9Gzebmf100IIUTFkyRSiDuIg7Or5bxuQgghxE0m+5iEEEIIIYTNJIkUQgghhBA2kyRSCCGEEKKMjEYjb7/9NkFBQTg6OlK7dm2mTZuG2Xz52uxms5mJEyfi6+uLo6MjnTt35vjx4+r63NxcXnjhBdzc3KhXrx5//vmn1TY++OADRowYccv6dL3kmEghhBBCiDJ6//33+fTTT1m6dCkNGzZk9+7dDBgwAHd3d1599VUAZs6cyccff8zSpUsJCgri7bffJiwsjEOHDuHg4MCiRYuIiooiMjKSP/74g+eee474+HgURSEmJobPP/+c3bt3V3BPr61CRyI//fRTmjRpgpubG25uboSGhvLHH3+o69u3b4+iKFbL0KFDrdqIjY2lR48eODk54eXlxZgxYzAYDFZ1Nm/eTIsWLbC3t6dOnTosWbKkWCzz58+nZs2aODg40Lp1a3bt2nVT+iyEEEKI29f27dt57LHH6NGjBzVr1uTJJ5+kS5cuat5gNpuZM2cOEyZM4LHHHqNJkyZ8/fXXnD9/nl9++QWAw4cP8+ijj9KwYUPCw8NJTEzk4sWLAAwbNoz3338fNze3iupimVXoSGSNGjV47733qFu3LmazmaVLl/LYY4+xZ88eGjZsCMDgwYOZOnWq+hgnJyf1ttFopEePHvj4+LB9+3YuXLhAv379sLOz49133wUgJiaGHj16MHToUJYtW8aGDRsYNGgQvr6+hIWFAfD9998TERHBwoULad26NXPmzCEsLIyjR4/i5eVlU58MBgP5+fkAaDQatFotRqMRk8mk1iksNxgMVsPfWq0WjUZTanlhu4V0Op26zbKU29nZYTKZMBqNapmiKOh0ulLLS4td+lT5+mQwGNBgQjEbAQWzokExm4Aiu1hQoMRyDSjKVcovb1MtBxRMVy3XYFL7J6+T9Olu7JPRaCQ1NVUtVxSlWIxarRbAKparlet0Osxmc7HYtVotJpPJKsbSyqtUqYKdnd1V+3r5++TmfUeo5YoWzOYryi3fY4Xlhd8nBoOh3F8ngPT0dNLS0tT19vb22Nvbc6X777+fRYsWcezYMerVq8fevXvZtm0bs2bNAix5R1xcHJ07d1Yf4+7uTuvWrYmMjKRPnz40bdqUb775huzsbNauXYuvry/VqlVj2bJlODg48PjjjxfbbmVUoUnkI488YnX/nXfe4dNPP2XHjh1qEunk5ISPj0+Jj1+3bh2HDh3izz//xNvbm2bNmjFt2jTeeOMNJk+ejF6vZ+HChQQFBfHRRx8B0KBBA7Zt28bs2bPVJHLWrFkMHjyYAQMGALBw4UJWr17N4sWLGTdunE19ioyMVBPdgIAAmjdvzr59+4iNjVXr1K9fn+DgYHbt2kViYqJa3qxZMwIDA9m6dSvp6elqeWhoKF5eXqxbt87qy6dDhw44Ojry+++/W8XQvXt3srOz2bRpk1qm0+no0aMHFy9eJDIyUi13dXWlY8eOnDlzhujoaLW8evXq3H///Rw/fpyjR4+q5dKnyt2nhjogO540rTsX7f2omheHmzFVrZ+sq0ayvjreuWdxMmWq5Yl6H9J1VbgnJwa9OU8tv2DvT7bWhcDsE2iKfLmfcaiFQdERlH3Mqk8xjvXQmQ3455wq6BDs2nWRnj17yuskfbrr+xRj9CTDbE+INg6tcjmZOWaoRj5aGurirfp00OCNHUbq6S6qZUazwiGjDy5KLkHaJLU8x6zjuLE6VZQsamgvf+bTTXpOm6ripUnHW5Ohlnt7e9OmTZtS+3To0CEa6lIg2xLTTfuOAExoOO1UH0dTJr65Z9TyPEXPWcfauBpTqJ4XBzqIjIwv99ep8D0QEhJiFeukSZOYPHkyVxo3bhxpaWkEBwerSes777xD3759AYiLi1Of46K8vb3VdS+99BL79u0jJCSEatWqsWLFCpKTk5k4cSKbN29mwoQJfPfdd9SuXZvFixdzzz33FIujMlDMRdPyCmQ0Glm5ciX9+/dnz549hISE0L59ew4ePIjZbMbHx4dHHnmEt99+W03SJk6cyK+//mr1JRATE0OtWrX4999/ad68Oe3ataNFixbMmTNHrfPVV18xatQoUlNTycvLw8nJiR9++IFevXqpdfr3709KSgr/+9//Sow3NzeX3Nxc9f65c+cICQkhJiZGfbHlP3Lp063s06VLl/jir1M4ubpTWUYis9JTGfRgLby8vOR1kj7dlX1KSkpSP5e3ctSueLkJpeCznZWeyuB2talevXqpfUpMTOTzrScLvk8qx0hk4feJp6dnub5Op0+fJigoiEOHDlkla6WNRH733XeMGTOGDz74gIYNGxIdHc2oUaOYNWsW/fv3Z/v27bRt25bz58/j6+urPu7pp59GURS+//77Ym0CDBgwgGbNmhEUFMSbb77Jzp07mTlzJgcOHODHH38s8TEVrcIn1uzfv5/Q0FBycnJwcXHh559/Vv8beO655wgMDMTPz499+/bxxhtvcPToUX766SfAku2XlOkXrrtanbS0NLKzs0lOTsZoNJZY58iRI6XGPWPGDKZMmVKsXKfTYWdnZ1Wm1WrV3RJX1i1JaeVXtns95RqNpsRL0JVWXlrs0qfK1yedTocJjeXLuIBZKfmwZ9vLiz8vAGauXm5Coz5P8jpJn+Du61PJn8vr+zxZURQbyzVq6mdCo/bjan29Mm5L7OX7HVG22C3lhd8nhc93eb/3XF1dy3Qc4pgxYxg3bhx9+vQBoHHjxvz333/MmDGD/v37q3tP4+PjrZLI+Ph4mjVrVmKbmzZt4uDBg3zxxReMGTOG7t274+zszNNPP828efOuGVNFqfAksn79+kRHR5OamsoPP/xA//792bJlCyEhIQwZMkSt17hxY3x9fenUqRMnT56kdu3aFRg1jB8/noiICPV+4UikEEIIIe5cWVlZxf75KDzuFCAoKAgfHx82bNigJo1paWns3LmTYcOGFWsvJyeH8PBwli1bpo6kFo6Y5ufnFzsmtjKp8PNE6vV66tSpQ8uWLZkxYwZNmzZl7ty5JdZt3bo1ACdOnADAx8eH+HjrY0gK7xf+J1BaHTc3NxwdHalWrRparbbEOqUdiwmWYe7CWeVubm64ulae6xULIYQQ4uZ45JFHeOedd1i9ejWnT5/m559/ZtasWepkGEVRGDVqFNOnT+fXX39l//799OvXDz8/P6vD5gpNmzaN7t2707x5cwDatm3LTz/9xL59+5g3bx5t27a9ld2zSYWPRF7JZDJZHWtYVOGxj4XDw6GhobzzzjskJCSos6jXr1+Pm5ubOioYGhpa7ADs9evXExoaCliS2JYtW7Jhwwb1xTWZTGzYsIHhw4eXd/eEEEIIcRv75JNPePvtt3nllVdISEjAz8+Pl19+mYkTJ6p1xo4dS2ZmJkOGDCElJYUHHniANWvW4ODgYNXWgQMHWLFihdXcjieffJLNmzfz4IMPUr9+fZYvX36rumazCk0ix48fT7du3QgICCA9PZ3ly5ezefNm1q5dy8mTJ1m+fDndu3enatWq7Nu3j9GjR9OuXTuaNGkCQJcuXQgJCeGFF15g5syZxMXFMWHCBMLDw9WDYYcOHcq8efMYO3YsL730Ehs3bmTFihWsXr1ajSMiIoL+/fvTqlUr7rvvPubMmUNmZqY6W1sIIYQQAizHTs6ZM8dqwu6VFEVh6tSpVqcoLEmjRo2srmQDlmN1FyxYwIIFC8oj3JuqQpPIhIQE+vXrx4ULF3B3d6dJkyasXbuWhx9+mDNnzvDnn3+qCZ2/vz+9e/dmwoQJ6uO1Wi2rVq1i2LBhhIaG4uzsTP/+/a1etKCgIFavXs3o0aOZO3cuNWrU4IsvvlBP7wPwzDPPkJiYyMSJE4mLi6NZs2asWbOm2GQbIYQQQghhUaFJ5JdfflnqOn9/f7Zs2XLNNgIDA4vtrr5S+/bt2bNnz1XrDB8+XHZfCyGEEEKUUYVPrBFCCCGEELcfSSKFEEIIIYTNJIkUQgghhBA2kyRSCCGEEELYTJJIIYQQQghhM0kihRBCCCGEzSSJFEIIIYQQNpMkUgghhBBC2EySSCGEEEIIYTNJIoUQQgghhM0kiRRCCCGEEDaTJFIIIYQQQthMkkghhBBCCGEzSSKFEEIIIYTNJIkUQgghhBA2kyRSCCGEEELYTJJIIYQQQghhM0kihRBCCCGEzSSJFEIIIYQQNpMkUgghhBBC2EySSCGEEEIIYTNJIoUQQgghhM0kiRRCCCGEEDaTJFIIIYQQQthMkkghhBBCCGEzSSKFEEIIIYTNbjiJTEtL45dffuHw4cPlEY8QQgghhLgN2JxEPv3008ybNw+A7OxsWrVqxdNPP02TJk348ccfyz1AIYQQQghR+dicRG7dupUHH3wQgJ9//hmz2UxKSgoff/wx06dPL/cAhRBCCCFE5WNzEpmamoqnpycAa9asoXfv3jg5OdGjRw+OHz9e7gEKIYQQQojKx+Yk0t/fn8jISDIzM1mzZg1dunQBIDk5GQcHh3IPUAghhBBCVD42J5GjRo2ib9++1KhRAz8/P9q3bw9YdnM3btzYprY+/fRTmjRpgpubG25uboSGhvLHH3+o63NycggPD6dq1aq4uLjQu3dv4uPjrdqIjY2lR48eODk54eXlxZgxYzAYDFZ1Nm/eTIsWLbC3t6dOnTosWbKkWCzz58+nZs2aODg40Lp1a3bt2mVTX4QQQggh7iY2J5GvvPIKkZGRLF68mG3btqHRWJqoVauWzcdE1qhRg/fee4+oqCh2795Nx44deeyxxzh48CAAo0eP5rfffmPlypVs2bKF8+fP88QTT6iPNxqN9OjRg7y8PLZv387SpUtZsmQJEydOVOvExMTQo0cPOnToQHR0NKNGjWLQoEGsXbtWrfP9998TERHBpEmT+Pfff2natClhYWEkJCTY+vQIIYQQQtwVFLPZbK7oIIry9PTkgw8+4Mknn6R69eosX76cJ598EoAjR47QoEEDIiMjadOmDX/88Qc9e/bk/PnzeHt7A7Bw4ULeeOMNEhMT0ev1vPHGG6xevZoDBw6o2+jTpw8pKSmsWbMGgNatW3Pvvfeqs85NJhP+/v6MGDGCcePGlSnus2fP4u/vz5kzZ6hRo0Z5PiVClElSUhILNp3A2c2jokNRZaal8EqHOupx1ELcbW7Xz+XtGvf1kN/v66ez9QFGo5ElS5awYcMGEhISMJlMVus3btx4XYEYjUZWrlxJZmYmoaGhREVFkZ+fT+fOndU6wcHBBAQEqElkZGQkjRs3VhNIgLCwMIYNG8bBgwdp3rw5kZGRVm0U1hk1ahQAeXl5REVFMX78eHW9RqOhc+fOREZGlhpvbm4uubm56v309PTr6rcQQgghxO3I5iRy5MiRLFmyhB49etCoUSMURbmhAPbv309oaCg5OTm4uLjw888/ExISQnR0NHq9Hg8PD6v63t7exMXFARAXF2eVQBauL1x3tTppaWlkZ2eTnJyM0Wgssc6RI0dKjXvGjBlMmTLluvoshBBCCHG7szmJ/O6771ixYgXdu3cvlwDq169PdHQ0qamp/PDDD/Tv358tW7aUS9s30/jx44mIiFDvnzt3jpCQkAqMSAghhBDi1rE5idTr9dSpU6fcAijaXsuWLfnnn3+YO3cuzzzzDHl5eaSkpFiNRsbHx+Pj4wOAj49PsVnUhbO3i9a5ckZ3fHw8bm5uODo6otVq0Wq1JdYpbKMk9vb22Nvbq/fT0tJs7LkQQgghxO3L5tnZr732GnPnzuVmzccxmUzk5ubSsmVL7Ozs2LBhg7ru6NGjxMbGEhoaCkBoaCj79++3mkW9fv163Nzc1FHB0NBQqzYK6xS2odfradmypVUdk8nEhg0b1DpCCCGEEMKazSOR27ZtY9OmTfzxxx80bNgQOzs7q/U//fRTmdsaP3483bp1IyAggPT0dJYvX87mzZtZu3Yt7u7uDBw4kIiICDw9PXFzc2PEiBGEhobSpk0bALp06UJISAgvvPACM2fOJC4ujgkTJhAeHq6OEg4dOpR58+YxduxYXnrpJTZu3MiKFStYvXq1GkdERAT9+/enVatW3HfffcyZM4fMzEwGDBhg69MjhBBCCHFXsDmJ9PDw4PHHHy+XjSckJNCvXz8uXLiAu7s7TZo0Ye3atTz88MMAzJ49G41GQ+/evcnNzSUsLIwFCxaoj9dqtaxatYphw4YRGhqKs7Mz/fv3Z+rUqWqdoKAgVq9ezejRo5k7dy41atTgiy++ICwsTK3zzDPPkJiYyMSJE4mLi6NZs2asWbOm2GQbIYQQFcNkMpGSklLRYRTj4eGhni9ZiLtNpTtP5O1KzjMlKtrddF43cfdJSkpi1qo9ODi7VnQoqpzMdCJ6Nr8jz7d4u8Z9PeT3+/rZPBJZKDExkaNHjwKWGdbVq1cvt6CEEEKIKzk4u1aqpEaIu53NY/CZmZm89NJL+Pr60q5dO9q1a4efnx8DBw4kKyvrZsQohBBCCCEqGZuTyIiICLZs2cJvv/1GSkoKKSkp/O9//2PLli289tprNyNGIYQQQghRydi8O/vHH3/khx9+oH379mpZ9+7dcXR05Omnn+bTTz8tz/iEEEIIIUQlZPNIZFZWVomzlr28vGR3thBCCCHEXcLmJDI0NJRJkyaRk5OjlmVnZzNlyhQ5ObcQQgghxF3C5t3Zc+fOJSwsjBo1atC0aVMA9u7di4ODA2vXri33AIUQQgghROVjcxLZqFEjjh8/zrJlyzhy5AgAzz77LH379sXR0bHcAxRCCCGEEJXPdZ0n0snJicGDB5d3LEIIIYQQ4jZRpiTy119/pVu3btjZ2fHrr79ete6jjz5aLoEJIYQQQojKq0xJZK9evYiLi8PLy4tevXqVWk9RFIxGY3nFJoQQQgghKqkyJZEmk6nE20IIIYQQ4u5k8yl+vv76a3Jzc4uV5+Xl8fXXX5dLUEIIIYQQonKzOYkcMGAAqampxcrT09MZMGBAuQQlhBBCCCEqN5uTSLPZjKIoxcrPnj2Lu7t7uQQlhBBCCCEqtzKf4qd58+YoioKiKHTq1Amd7vJDjUYjMTExdO3a9aYEKYQQQgghKpcyJ5GFs7Kjo6MJCwvDxcVFXafX66lZsya9e/cu9wCFEEIIIUTlU+YkctKkSQDUrFmTPn36YG9vf9OCEkIIIYQQlZvNx0SGhIQQHR1drHznzp3s3r27PGISQgghhBCVnM1JZHh4OGfOnClWfu7cOcLDw8slKCGEEEKIyurcuXM8//zzVK1aFUdHRxo3bmw1kGY2m5k4cSK+vr44OjrSuXNnjh8/rq7Pzc3lhRdewM3NjXr16vHnn39atf/BBx8wYsSIW9af62VzEnno0CFatGhRrLx58+YcOnSoXIISQgghhKiMkpOTadu2LXZ2dvzxxx8cOnSIjz76iCpVqqh1Zs6cyccff8zChQvZuXMnzs7OhIWFkZOTA8CiRYuIiooiMjKSIUOG8Nxzz2E2mwGIiYnh888/55133qmQ/tmizMdEFrK3tyc+Pp5atWpZlV+4cMFqxrYQQgghxJ3m/fffx9/fn6+++kotCwoKUm+bzWbmzJnDhAkTeOyxxwDLhVq8vb355Zdf6NOnD4cPH+bRRx+lYcOG1KpVizFjxnDx4kWqV6/OsGHDeP/993Fzc7vlfbOVzVlfly5dGD9+PP/73//U80KmpKTw5ptv8vDDD5d7gLcbg8FAfn4+ABqNBq1Wi9FotLpcZGG5wWBQ//MA0Gq1aDSaUssL2y1UmLQbDIYyldvZ2WEymayub64oCjqdrtTy0mKXPlW+PhkMBjSYUMxGQMGsaFDMJuByjGYUKLFcA4pylfLL21TLAQXTVcs1mNT+yeskfbqRPhW+vymoc633nlquaMFsvqLc8vkovdyEUuRzUNrnqbDO1fpU9HN5o5+n8uqThsuv5dVev8vfJzfvO8KWPhV+nxgMhnJ/74HloilpaWnqent7+xInEf/666+EhYXx1FNPsWXLFu655x5eeeUVBg8eDFhGEuPi4ujcubP6GHd3d1q3bk1kZCR9+vShadOmfPPNN2RnZ7N27Vp8fX2pVq0ay5Ytw8HBgccff7zYdisjm5PIDz/8kHbt2hEYGEjz5s0By2l/vL29+eabb8o9wNtNZGQkTk5OAAQEBNC8eXP27dtHbGysWqd+/foEBweza9cuEhMT1fJmzZoRGBjI1q1bSU9PV8tDQ0Px8vJi3bp1Vl/6HTp0wNHRkd9//90qhu7du5Odnc2mTZvUMp1OR48ePbh48SKRkZFquaurKx07duTMmTNWE6aqV6/O/fffz/Hjxzl69KhaLn2q3H1qqAOy40nTunPR3o+qeXG4GS9fYSpZV41kfXW8c8/iZMpUyxP1PqTrqnBPTgx6c55afsHen2ytC4HZJyw/4AXOONTCoOgIyj5m1acYx3rozAb8c04VdAh27bpIz5495XWSPt1wnxrq4IzZuWzvPcCEhtNO9XE0ZeKbe/lY/jxFz1nH2rgaU6ieF6eWZ2mciXMIoEr+JaoYLqrlpX2e4jWWU91dq0+Fn8sb/jyVV590cPKk5TUs7XU6dOgQDXUpkB0P3MTvCFv6pIPIyPhyf+8Vvq9DQkKsYp00aRKTJ0/mSqdOneLTTz8lIiKCN998k3/++YdXX30VvV5P//79iYuzPP/e3t5Wj/P29lbXvfTSS+zbt4+QkBCqVavGihUrSE5OZuLEiWzevJkJEybw3XffUbt2bRYvXsw999xTLI7KQDEXTcvLKDMzk2XLlrF3714cHR1p0qQJzz77LHZ2djcjxtvC2bNn8ff3JyYmRn2xZZRB+nQr+3Tp0iW++OsUTq7uVJaRyKz0VAY9WAsvLy95naRPN9Sn5ORkvvjrFI6uluPOKsNIZGZ6GsM61MXNza3UPiUlJamfy8oyEpmVnsrgdrWpXr16qa9TYmIin289WfB9UjlGIgu/Tzw9Pcv1vXf69GmCgoI4dOiQVbJW2kikXq+nVatWbN++XS179dVX+eeff4iMjGT79u20bduW8+fP4+vrq9Z5+umnURSF77//vlibYLmsdLNmzQgKCuLNN99k586dzJw5kwMHDvDjjz+W+JiKdl0HMTo7OzNkyJDyjuWOoNPpiiXTWq0WrVZbYt3S2ihJaUm6LeUajQaNpvh8qtLKS4td+lT5+qTT6TChsXwZFzArJc+ds728+PMCYObq5SY06vMkr5P0Ca6/T4Xvbwouu3ut954VRbGxXENJoytXfj7MKFeNXafTlfK5vL7PU9liv3afTGjU1+Zqr9+VcVtiL9/viLLFbikv/D4pfL7L+73n6upapuMQfX19i41aNmjQQE30fHx8AIiPj7dKIuPj42nWrFmJbW7atImDBw/yxRdfMGbMGLp3746zszNPP/008+bNu2ZMFaVMSeSvv/5Kt27dsLOz49dff71q3UcffbRcAhNCCCGEqGzatm1rtRsd4NixYwQGBgKWSTY+Pj5s2LBBTRrT0tLYuXMnw4YNK9ZeTk4O4eHhLFu2TB1JLRwxzc/Pt9oDUNmUKYns1asXcXFxeHl5qZc/LImiKJW6s0IIIYQQN2L06NHcf//9vPvuuzz99NPs2rWLRYsWsWjRIsCSC40aNYrp06dTt25dgoKCePvtt/Hz8ysxh5o2bRrdu3dX55m0bduWMWPGMGDAAObNm0fbtm1vZfdsUqYksuixBUVvCyGEEELcTe69915+/vlnxo8fz9SpUwkKCmLOnDn07dtXrTN27FgyMzMZMmQIKSkpPPDAA6xZswYHBwertg4cOMCKFSusJq09+eSTbN68mQcffJD69euzfPnyW9U1m8mJHYUQQgghbNCzZ0969uxZ6npFUZg6dSpTp069ajuNGjWyupINWI4/XrBgAQsWLCiXWG+mMiWRH3/8cZkbfPXVV687GCGEEEIIcXsoUxI5e/Zsq/uJiYlkZWXh4eEBWE427uTkhJeXl01J5IwZM/jpp584cuQIjo6O3H///bz//vvUr19frdO+fXu2bNli9biXX36ZhQsXqvdjY2MZNmwYmzZtwsXFhf79+zNjxgyrmVibN28mIiKCgwcP4u/vz4QJE3jxxRet2p0/fz4ffPABcXFxNG3alE8++YT77ruvzP0RQgghhKisjEYjS5YsYcOGDSQkJBQ7RHHjxo02tVema2fHxMSoyzvvvEOzZs04fPgwSUlJJCUlcfjwYVq0aMG0adNs2viWLVsIDw9nx44drF+/nvz8fLp06UJmZqZVvcGDB3PhwgV1mTlzprrOaDTSo0cP8vLy2L59O0uXLmXJkiVMnDjRKv4ePXrQoUMHoqOjGTVqFIMGDWLt2rVqne+//56IiAgmTZrEv//+S9OmTQkLCyMhIcGmPgkhhBBCVEYjR45k5MiRGI1GGjVqRNOmTa0WW9l8TOTbb7/NDz/8YDVaWL9+fWbPns2TTz5pdWDptaxZs8bq/pIlS/Dy8iIqKop27dqp5U5OTup5l660bt06Dh06xJ9//om3tzfNmjVj2rRpvPHGG0yePBm9Xs/ChQsJCgrio48+Aiznc9q2bRuzZ88mLCwMgFmzZjF48GAGDBgAwMKFC1m9ejWLFy9m3LhxZe6TEEIIIURl9N1337FixQq6d+9eLu2VaSSyqAsXLhS7ygFYRgTj4+NvKJjUVMvlpDw9Pa3Kly1bRrVq1WjUqBHjx48nKytLXRcZGUnjxo2tLi8UFhZGWloaBw8eVOsUvYZlYZ3CS3vl5eURFRVlVUej0dC5c2ery38VlZubS1pamroUvVyXEEIIIURlo9frqVOnTrm1Z3MS2alTJ15++WX+/fdftSwqKophw4YVS9RsYTKZGDVqFG3btqVRo0Zq+XPPPcf//d//sWnTJsaPH88333zD888/r66Pi4sr8fqUheuuVictLY3s7GwuXryI0Wi86nUurzRjxgzc3d3V5cqz1wshhBBCVCavvfYac+fO5TqueF0im3dnL168mP79+9OqVSv1clQGg4GwsDC++OKL6w4kPDycAwcOsG3bNqvyopdXbNy4Mb6+vnTq1ImTJ09Su3bt697ejRo/fjwRERHq/XPnzkkiKYQQQohKa9u2bWzatIk//viDhg0bFrv06U8//WRTezYnkdWrV+f333/n2LFjHDlyBIDg4GDq1atna1Oq4cOHs2rVKrZu3UqNGjWuWrd169YAnDhxgtq1a+Pj48OuXbus6hTuVi88jtLHx6fYrvb4+Hjc3NxwdHRUr7NZUp3SjsW88sLsaWlpZeipEEIIIUTF8PDw4PHHHy+39q77ZOM1a9bEbDZTu3btUi9qfi1ms5kRI0bw888/s3nzZoKCgq75mMKzuhde1Dw0NJR33nmHhIQEvLy8AFi/fj1ubm7qyGBoaCi///67VTvr168nNDQUsBwj0LJlSzZs2KBekshkMrFhwwaGDx9+XX0TQgghhKhMvvrqq3Jtz+ZjIrOyshg4cCBOTk40bNiQ2NhYAEaMGMF7771nU1vh4eH83//9H8uXL8fV1ZW4uDji4uLIzs4G4OTJk0ybNo2oqChOnz7Nr7/+Sr9+/WjXrh1NmjQBoEuXLoSEhPDCCy+wd+9e1q5dy4QJEwgPD1dHCocOHcqpU6cYO3YsR44cYcGCBaxYsYLRo0ersURERPD555+zdOlSDh8+zLBhw8jMzFRnawshhBBC3AkSExPZtm0b27ZtIzEx8brbsTmJHD9+PHv37mXz5s1W14Ds3Lkz33//vU1tffrpp6SmptK+fXt8fX3VpbAdvV7Pn3/+SZcuXQgODua1116jd+/e/Pbbb2obWq2WVatWodVqCQ0N5fnnn6dfv35WlxoKCgpi9erVrF+/nqZNm/LRRx/xxRdfqKf3AXjmmWf48MMPmThxIs2aNSM6Opo1a9YUm2wjhBBCCHE7yszM5KWXXsLX15d27drRrl07/Pz8GDhwoNWZb8rK5v3Qv/zyC99//z1t2rRBURS1vGHDhpw8edKmtq41O8jf37/Y1WpKEhgYWGx39ZXat2/Pnj17rlpn+PDhsvtaCCGEEHekiIgItmzZwm+//Ubbtm0By2SbV199lddee41PP/3UpvZsTiITExPVYw+LyszMtEoqhRBCCCFE5fHjjz/yww8/0L59e7Wse/fuODo68vTTT9ucRNq8O7tVq1asXr1avV+YOH7xxRfqRBUhhBBCCFG5ZGVllXiYnpeX163Znf3uu+/SrVs3Dh06hMFgYO7cuRw6dIjt27eXadezEEIIIYS49UJDQ5k0aRJff/21Oq8lOzubKVOmXNdAoM1J5AMPPMDevXuZMWMGjRs3Zt26dbRo0UK9/KAQQgghhKh85s6dS1hYGDVq1KBp06YA7N27FwcHB9auXWtzezYlkfn5+bz88su8/fbbfP755zZvTAghhBBCVIxGjRpx/Phxli1bpl4w5tlnn6Vv3744Ojra3J5NSaSdnR0//vgjb7/9ts0bEkIIIYQQFcvJyYnBgweXS1s2787u1asXv/zyi9WJuoUQQgghROXz66+/0q1bN+zs7Pj111+vWvfRRx+1qW2bk8i6desydepU/v77b1q2bImzs7PV+ldffdXWJoUQQgghxE3Qq1cv4uLi8PLyUi/tXBJFUTAajTa1bXMS+eWXX+Lh4UFUVBRRUVHFApAkUgghhBCicjCZTCXeLg82J5ExMTHlGoAQQgghhLj5vv76a5555hns7e2tyvPy8vjuu+/o16+fTe3ZnEQWVXjZQrlSjbiTmEwmUlJSKjqMYjw8PNBobL4+gBBCCAHAgAED6Nq1a7ErD6anpzNgwIBbk0R++eWXzJ49m+PHjwOW4yRHjRrFoEGDrqc5ISqVlJQUZq3ag4Oza0WHosrJTCeiZ3M8PT0rOhQhhBC3KbPZXOLA39mzZ3F3d7e5PZuTyIkTJzJr1ixGjBihnt08MjKS0aNHExsby9SpU20OQojKxsHZFWc3j4oOQwghhLhhzZs3R1EUFEWhU6dO6HSX0z+j0UhMTAxdu3a1uV2bk8hPP/2Uzz//nGeffVYte/TRR2nSpAkjRoyQJFIIIYQQohIpnJUdHR1NWFgYLi4u6jq9Xk/NmjXp3bu3ze3anETm5+fTqlWrYuUtW7bEYDDYHIAQQgghhLh5Jk2aBEDNmjV55pln1Otm3yibj9J/4YUX+PTTT4uVL1q0iL59+5ZLUEIIIYQQonz1798fBwcH8vLyOHv2LLGxsVaLra57Ys26deto06YNADt37iQ2NpZ+/foRERGh1ps1a9b1NC+EEEIIIcrZ8ePHeemll9i+fbtVeeGEm5t+svEDBw7QokULAE6ePAlAtWrVqFatGgcOHFDryWl/hBBCCCEqjxdffBGdTseqVavw9fW94VzN5iRy06ZNN7RBIYQQQghx60VHRxMVFUVwcHC5tCdnLhZCCCGEuAuEhIRw8eLFcmtPkkghhBBCiLvA+++/z9ixY9m8eTOXLl0iLS3NarHVDV32UAghhBBC3B46d+4MQKdOnazKb9nEGiGEEEIIcfsp73ktZUoiW7RowYYNG6hSpQpTp07l9ddfx8nJqVwDEUIIIYQQN89DDz1Uru2V6ZjIw4cPk5mZCcCUKVPIyMgo1yCEEEIIIcTN99dff/H8889z//33c+7cOQC++eYbtm3bZnNbZRqJbNasGQMGDOCBBx7AbDbz4YcfWl13saiJEyfaHIQQQgghhLi5fvzxR1544QX69u3Lv//+S25uLgCpqam8++67/P777za1V6YkcsmSJUyaNIlVq1ahKAp//PEHOl3xhyqKIkmkEEIIIUQlNH36dBYuXEi/fv347rvv1PK2bdsyffp0m9srUxJZv359dWMajYYNGzbg5eVl88aEEEIIIUTFOHr0KO3atStW7u7uTkpKis3t2XyeSJPJJAmkEEIIIcRtxsfHhxMnThQr37ZtG7Vq1bK5ves6xc/JkyeZM2cOhw8fBixnQB85ciS1a9e+nuaEEEIIIcRNNnjwYEaOHMnixYtRFIXz588TGRnJ66+/zttvv21zezYnkWvXruXRRx+lWbNmtG3bFoC///6bhg0b8ttvv/Hwww/bHIQQQgghhLi5xo0bh8lkolOnTmRlZdGuXTvs7e15/fXXGTFihM3t2bw7e9y4cYwePZqdO3cya9YsZs2axc6dOxk1ahRvvPGGTW3NmDGDe++9F1dXV7y8vOjVqxdHjx61qpOTk0N4eDhVq1bFxcWF3r17Ex8fb1UnNjaWHj164OTkhJeXF2PGjMFgMFjV2bx5My1atMDe3p46deqwZMmSYvHMnz+fmjVr4uDgQOvWrdm1a5dN/RFCCCGEqKwUReGtt94iKSmJAwcOsGPHDhITE5k2bdp1tWfzSOThw4dZsWJFsfKXXnqJOXPm2NTWli1bCA8P595778VgMPDmm2/SpUsXDh06hLOzMwCjR49m9erVrFy5End3d4YPH84TTzzB33//DYDRaKRHjx74+Piwfft2Lly4QL9+/bCzs+Pdd98FICYmhh49ejB06FCWLVvGhg0bGDRoEL6+voSFhQHw/fffExERwcKFC2ndujVz5swhLCyMo0ePyjGg18lkMl3Xgbo3m4eHBxqNXDZeCCHE3eWll15i7ty5uLq6EhISopZnZmYyYsQIFi9ebFN7NieR1atXJzo6mrp161qVR0dH25xsrVmzxur+kiVL8PLyIioqinbt2pGamsqXX37J8uXL6dixIwBfffUVDRo0YMeOHbRp04Z169Zx6NAh/vzzT7y9vWnWrBnTpk3jjTfeYPLkyej1ehYuXEhQUBAfffQRAA0aNGDbtm3Mnj1bTSJnzZrF4MGDGTBgAAALFy5k9erVLF68mHHjxtn6NAkgJSWFWav24ODsWtGhqHIy04no2RxPT8+KDkUIIYS4pZYuXcp7772Hq6v173J2djZff/31zU8iBw8ezJAhQzh16hT3338/YDkm8v333yciIsLW5qykpqYCqD/wUVFR5OfnqxcMBwgODiYgIIDIyEjatGlDZGQkjRs3xtvbW60TFhbGsGHDOHjwIM2bNycyMtKqjcI6o0aNAiAvL4+oqCjGjx+vrtdoNHTu3JnIyMgSY83NzVVP0gmQnp5+Q32/Uzk4u+Ls5lHRYQghhBB3rbS0NMxmM2azmfT0dBwcHNR1RqOR33///br2utqcRL799tu4urry0UcfqUmXn58fkydP5tVXX7U5gEImk4lRo0bRtm1bGjVqBEBcXBx6vR4PDw+rut7e3sTFxal1iiaQhesL112tTlpaGtnZ2SQnJ2M0Gkusc+TIkRLjnTFjBlOmTLm+zgohhBBC3CIeHh4oioKiKNSrV6/YekVRriunsTmJVBSF0aNHM3r0aHX07cph0esRHh7OgQMHruvajRVh/PjxViOv586dszq+QAghhBCiMti0aRNms5mOHTvy448/Wh3SpdfrCQwMxM/Pz+Z2r+s8kYXKI3kEGD58OKtWrWLr1q3UqFFDLffx8SEvL4+UlBSr0cj4+Hh8fHzUOlfOoi6cvV20zpUzuuPj43Fzc8PR0RGtVotWqy2xTmEbV7K3t8fe3l69n5aWZmOvhRBCCCFuvoceegiwTDQOCAhAUZRyabdCp6iazWaGDx/Ozz//zMaNGwkKCrJa37JlS+zs7NiwYYNadvToUWJjYwkNDQUgNDSU/fv3k5CQoNZZv349bm5u6shgaGioVRuFdQrb0Ov1tGzZ0qqOyWRiw4YNah0hhBBCiNtZYGAg27Zt4/nnn+f+++/n3LlzAHzzzTfXtSe4QpPI8PBw/u///o/ly5fj6upKXFwccXFxZGdnA5ZrOQ4cOJCIiAg2bdpEVFQUAwYMIDQ0lDZt2gDQpUsXQkJCeOGFF9i7dy9r165lwoQJhIeHqyOFQ4cO5dSpU4wdO5YjR46wYMECVqxYwejRo9VYIiIi+Pzzz1m6dCmHDx9m2LBhZGZmqrO1hRBCCCFuZz/++CNhYWE4Ojry77//qhOEU1NT1dMi2qJCk8hPP/2U1NRU2rdvj6+vr7p8//33ap3Zs2fTs2dPevfuTbt27fDx8eGnn35S12u1WlatWoVWqyU0NJTnn3+efv36MXXqVLVOUFAQq1evZv369TRt2pSPPvqIL774Qj29D8AzzzzDhx9+yMSJE2nWrBnR0dGsWbOm2GQbIYQQQojb0fTp01m4cCGff/45dnZ2annbtm35999/bW7PpmMi8/Pz6dq1KwsXLix2nsjrYTabr1nHwcGB+fPnM3/+/FLrBAYG8vvvv1+1nfbt27Nnz56r1hk+fDjDhw+/ZkxCCCGEELebo0eP0q5du2Ll7u7u13VxEJtGIu3s7Ni3b5/NGxFCCCGEEBXLx8eHEydOFCvftm0btWrVsrk9m3dnP//883z55Zc2b0gIIYQQQlScwYMHM3LkSHbu3ImiKJw/f55ly5bx2muvMWzYMJvbs/kUPwaDgcWLF/Pnn3/SsmVL9RrXhWbNmmVzEEIIIYQQ4uYaN24cJpOJTp06kZWVRbt27bC3t2fMmDEMGjTI5vZsTiIPHDhAixYtADh27JjVuvI675AQQgghhChfiqLw1ltvMWbMGE6cOEFGRgYhISF89tlnBAUFqVf6Kyubk8hNmzbZ+hAhhBBCCFFBcnNzmTx5MuvXr1dHHnv16sVXX33F448/jlartTrtYVld9xVrTpw4wcmTJ2nXrh2Ojo6YzWYZiRRCCCGEqGQmTpzIZ599RufOndm+fTtPPfUUAwYMYMeOHXz00Uc89dRTaLVam9u1OYm8dOkSTz/9NJs2bUJRFI4fP06tWrUYOHAgVapU4aOPPrI5CCGEEEIIcXOsXLmSr7/+mkcffZQDBw7QpEkTDAYDe/fuvaEBQJtnZ48ePRo7OztiY2NxcnJSy5955hnWrFlz3YEIIYQQQojyd/bsWVq2bAlAo0aNsLe3Z/To0Te8B9nmkch169axdu1aatSoYVVet25d/vvvvxsKRgghhBBClC+j0Yher1fv63Q6XFxcbrhdm0ciMzMzrUYgCyUlJanXqhZCCCGEuBu89957KIrCqFGj1LKcnBzCw8OpWrUqLi4u9O7dm/j4eHV9UlISjzzyCC4uLjRv3rzYFfXCw8PL9fBAs9nMiy++yBNPPMETTzxBTk4OQ4cOVe8XLrayOYl88MEH+frrr9X7iqJgMpmYOXMmHTp0sDkAIYQQQojb0T///MNnn31GkyZNrMpHjx7Nb7/9xsqVK9myZQvnz5+3StLeeecd0tPT+ffff2nfvj2DBw9W1+3YsYOdO3daJaU3qn///nh5eeHu7o67uzvPP/88fn5+6v3CxVY2786eOXMmnTp1Yvfu3eTl5TF27FgOHjxIUlISf//9t80BCCGEEELcbjIyMujbty+ff/4506dPV8tTU1P58ssvWb58OR07dgTgq6++okGDBuzYsYM2bdpw+PBh+vTpQ7169RgyZAiLFi0CID8/n6FDh/LFF19c12zp0nz11Vfl1lZRNieRjRo14tixY8ybNw9XV1cyMjJ44oknCA8Px9fX92bEeFsxGAzk5+cDoNFo0Gq1GI1GTCaTWqew3GAwYDab1XKtVotGoym1vLDdQjqdTt1mWcrt7OwwmUwYjUa1TFEUdDpdqeWlxV6WPhkMBhQstxWzCbjcJzMaUBQU8+VtquWAgqls5YoWzOYryhXMiqbEcgCTyWT1XF7ZJ4PBgAYTmE2gaK4S+63rk6bg9tVep8K4Ldu3PAfFY1RuaZ80mNT34a1878Gd93m62/t0+XNZ8J1yk74jLOUm9buraPmVn4/COlfrU9HPZUV9713ZJw2XX8urvX6Xv08q5nvvyj4Vfp8YDIZyf+8BpKenk5aWpq63t7e/6mF64eHh9OjRg86dO1slkVFRUeTn59O5c2e1LDg4mICAACIjI2nTpg1NmzZl48aNDBo0iLVr16ojmTNnzqR9+/a0atWq1O1WJtd1nkh3d3feeuut8o7ljhAZGakeMxoQEEDz5s3Zt28fsbGxap369esTHBzMrl27SExMVMubNWtGYGAgW7duJT09XS0PDQ3Fy8uLdevWWX3pd+jQAUdHR37//XerGLp37052drbVieF1Oh09evTg4sWLREZGquWurq507NiRM2fOEB0drZZXr16d+++/n+PHj3P06FG13NY+eSju5FGFe3Ji0Jvz1PIL9v5ka10IzD6hJkgAZxxqYVB0BGVbXw0pxrEeOrMB/5xTapkJDaed6uNoysQ394xanqfoOetYG1djCtXzLp99P0vjTAZunDlzxmrUvKQ+NdRBcr6RZH11vHPP4mTKVOsn6n1I193aPuVoLR/Va71ODXVAdjxpWncu2vtRNS8ON2OqWj9ZV+3W9kkHu3ZdpGfPnrf8vXcnfp7u9j411MEZs/NN/Y6IcwigSv4lqhguquWlfZ7iNZaJCdfqU+HnsqK+94r1SQcnT1pew9Jep0OHDtFQlwLZluP4KuJ7r1ifdBAZGV/u773C93VISIhVrJMmTWLy5MmU5LvvvuPff//ln3/+KbYuLi4OvV6Ph4eHVbm3t7d6RZhx48YxbNgwateuTc2aNfnyyy85fvw4S5cuJTIykqFDh7Ju3TpatWrF559/fl27mm8FxVw0LS+j5ORkvvzySw4fPgxYnvgBAwbg6elZ7gHeLs6ePYu/vz8xMTHcc889gIwyJCcn8/lfMTi5Vak0I5EZ6WkMfaiW1Qfyyj4lJyfzxV+ncHT1qDQjkVnpqQztUA8PD49SX6dLly7xxV+ncHJ1p7SRk1s9EpmVnsqgB2vh5eVVaUe4ClX2z9Pd3qfLn8sqlu1XgpHIzPQ0hnWoi5ubW6l9SkpKUj+XlWUkMis9lcHtalO9evVSX6fExEQ+33qy4PukcoxEFn6feHp6lut77/Tp0wQFBXHo0CH19xtKH4k8c+YMrVq1Yv369eoIYvv27WnWrBlz5sxh+fLlDBgwgNzcXKvH3XfffXTo0IH333+/WJsAHTt2ZOTIkfz333+sWrWK1atXM3jwYKpWrVppz8Ft80jk1q1beeSRR3B3d1eHWz/++GOmTp3Kb7/9Rrt27co9yNuJTqfDzs7Oqkyr1ZZ4bEPhF3lZy69s93rKNRoNGk3x+VSllZcWe1n6pNPpLEkLWL4ISmBWSj7mw4wN5YpiU7lGoynxuSnsk06nw4QGCmIuPfZb1ydTwRfv1V6nwriLbt/22Mu3TyY06nviVr73ylJ+u32eylJ+J/fp8uey4DvlJn5HoGgoaXTlys9H4ffb1fpU8ufy1n/vFe2TCY362lzt9bsybkvsFfddXvh9Uvh8l/d7z9XVFTc3txLrFBUVFUVCQgItWrRQy4xGI1u3bmXevHmsXbuWvLw8UlJSrEYj4+Pj8fHxKbHNr776Cg8PDx577DGeeOIJevXqhZ2dHU899RQTJ068ZkwVxeYkMjw8nGeeeYZPP/1UfZGMRiOvvPIK4eHh7N+/v9yDFEIIIYSoDDp16lQs1xkwYADBwcG88cYb+Pv7Y2dnx4YNG+jduzcAR48eJTY2ltDQ0GLtJSYmMnXqVLZt2wZYcqrCEfj8/HyrPQCVjc1J5IkTJ/jhhx+ssnytVktERITVqX+EEEIIIe40rq6uNGrUyKrM2dmZqlWrquUDBw4kIiICT09P3NzcGDFiBKGhobRp06ZYe6NGjeK1115Td6W3bduWb775hi5durBo0SLatm178zt1nWw+T2SLFi3UYyGLOnz4ME2bNi2XoIQQQgghblezZ8+mZ8+e9O7dm3bt2uHj48NPP/1UrN7atWs5ceIEr7zyilo2fPhwatWqRevWrcnLy2PSpEm3MnSblGkkct++fertV199lZEjR3LixAk1o96xYwfz58/nvffeuzlRCiGEEEJUUps3b7a67+DgwPz585k/f/5VHxcWFkZYWJhVmZOTEytWrCjvEG+KMiWRzZo1Q1EUq1lNY8eOLVbvueee45lnnim/6IQQQgghRKVUpiQyJibmZschhBBCCCFuI2VKIgMDA292HEIIIYQQ4jZyXVesOX/+PNu2bSMhIcHqhJ5gOWZSCCGEEELc2WxOIpcsWcLLL7+MXq+natWqKAUnfgXLFQwkiRRCCCGEuPPZnES+/fbbTJw4kfHjx5d49QIhhBBCCHHnszkLzMrKok+fPpJACiGEEELcxWzOBAcOHMjKlStvRixCCCGEEOI2YfPu7BkzZtCzZ0/WrFlD48aNsbOzs1o/a9ascgtOCCGEEEJUTteVRK5du5b69esDFJtYI4QQQggh7nw2J5EfffQRixcv5sUXX7wJ4QghhBBCiNuBzcdE2tvb07Zt23LZ+NatW3nkkUfw8/NDURR++eUXq/UvvvgiiqJYLV27drWqk5SURN++fXFzc8PDw4OBAweSkZFhVWffvn08+OCDODg44O/vz8yZM4vFsnLlSoKDg3FwcKBx48b8/vvv5dJHIYQQQog7kc1J5MiRI/nkk0/KZeOZmZk0bdr0qhco79q1KxcuXFCXb7/91mp93759OXjwIOvXr2fVqlVs3bqVIUOGqOvT0tLo0qULgYGBREVF8cEHHzB58mQWLVqk1tm+fTvPPvssAwcOZM+ePfTq1YtevXpx4MCBcumnEEIIIcSdxubd2bt27WLjxo2sWrWKhg0bFptY89NPP5W5rW7dutGtW7er1rG3t8fHx6fEdYcPH2bNmjX8888/tGrVCoBPPvmE7t278+GHH+Ln58eyZcvIy8tj8eLF6PV6GjZsSHR0NLNmzVKTzblz59K1a1fGjBkDwLRp01i/fj3z5s1j4cKFZe6PEEIIIcTdwuaRSA8PD5544gkeeughqlWrhru7u9VS3jZv3oyXlxf169dn2LBhXLp0SV0XGRmJh4eHmkACdO7cGY1Gw86dO9U67dq1Q6/Xq3XCwsI4evQoycnJap3OnTtbbTcsLIzIyMhS48rNzSUtLU1d0tPTy6W/QgghhBC3A5tHIr/66qubEUeJunbtyhNPPEFQUBAnT57kzTffpFu3bkRGRqLVaomLi8PLy8vqMTqdDk9PT+Li4gCIi4sjKCjIqo63t7e6rkqVKsTFxallResUtlGSGTNmMGXKlPLophBCCCHEbcfmJPJW6tOnj3q7cePGNGnShNq1a7N582Y6depUgZHB+PHjiYiIUO+fO3eOkJCQCoxICCGEEOLWsTmJDAoKuur5IE+dOnVDAV1NrVq1qFatGidOnKBTp074+PiQkJBgVcdgMJCUlKQeR+nj40N8fLxVncL716pT2rGYYDlW097eXr2flpZ2/R0TQgghhLjN2JxEjho1yup+fn4+e/bsYc2aNerElJvl7NmzXLp0CV9fXwBCQ0NJSUkhKiqKli1bArBx40ZMJhOtW7dW67z11lvk5+erk4DWr19P/fr1qVKlilpnw4YNVn1bv349oaGhN7U/QgghhBC3K5uTyJEjR5ZYPn/+fHbv3m1TWxkZGZw4cUK9HxMTQ3R0NJ6ennh6ejJlyhR69+6Nj48PJ0+eZOzYsdSpU4ewsDAAGjRoQNeuXRk8eDALFy4kPz+f4cOH06dPH/z8/AB47rnnmDJlCgMHDuSNN97gwIEDzJ07l9mzZ1v16aGHHuKjjz6iR48efPfdd+zevdvqNEBCCCGEEOIym2dnl6Zbt278+OOPNj1m9+7dNG/enObNmwMQERFB8+bNmThxIlqtln379vHoo49Sr149Bg4cSMuWLfnrr7+sdiMvW7aM4OBgOnXqRPfu3XnggQeskj93d3fWrVtHTEwMLVu25LXXXmPixIlW55K8//77Wb58OYsWLaJp06b88MMP/PLLLzRq1OgGnxUhhBBCiDtTuU2s+eGHH/D09LTpMe3bt8dsNpe6fu3atddsw9PTk+XLl1+1TpMmTfjrr7+uWuepp57iqaeeuub2hBBCCCHEdSSRzZs3t5pYYzabiYuLIzExkQULFpRrcEIIIYQQonKyOYns1auX1X2NRkP16tVp3749wcHB5RWXEEIIIYSoxGxOIidNmnQz4hBCCCGEELeRcptYI4QQQggh7h5lHonUaDRXPck4gKIoGAyGGw5KCCGEEEJUbmVOIn/++edS10VGRvLxxx9jMpnKJSghhBBCCFG5lTmJfOyxx4qVHT16lHHjxvHbb7/Rt29fpk6dWq7BCSGEEEKIyum6jok8f/48gwcPpnHjxhgMBqKjo1m6dCmBgYHlHZ8QQgghhKiEbJqdnZqayrvvvssnn3xCs2bN2LBhAw8++ODNik0IcRcwmUykpKRUdBjFeHh4oNHI3EMhhChNmZPImTNn8v777+Pj48O3335b4u5tIYSwVUpKCrNW7cHB2bWiQ1HlZKYT0bO5zVfhEkKIu0mZk8hx48bh6OhInTp1WLp0KUuXLi2x3k8//VRuwQkh7g4Ozq44u3lUdBg2kRFUIcTdrsxJZL9+/a55ih8hhLhbyAiqEOJuV+YkcsmSJTcxDCGEuP3cjiOoQghRXmSfhxBCCCGEsJkkkUIIIYQQwmaSRAohhBBCCJtJEimEEEIIIWwmSaQQQgghhLCZJJFCCCGEEMJmkkQKIYQQQgibSRIphBBCCCFsJkmkEEIIIYSwmSSRQgghhBDCZpJECiGEEEIIm0kSKYQQQgghbCZJpBBCCCGEsJkkkUIIIYQQwmaSRAohhBBCCJvpKjoAIYQQt47JZCIlJaWiwyjGw8MDjUbGNYS4nUgSKYQQd5GUlBRmrdqDg7NrRYeiyslMJ6Jnczw9PSs6FCGEDSSJFEKIu4yDsyvObh4VHYYQ4jZXofsOtm7dyiOPPIKfnx+KovDLL79YrTebzUycOBFfX18cHR3p3Lkzx48ft6qTlJRE3759cXNzw8PDg4EDB5KRkWFVZ9++fTz44IM4ODjg7+/PzJkzi8WycuVKgoODcXBwoHHjxvz+++/l3l8hhBBCiDtFhSaRmZmZNG3alPnz55e4fubMmXz88ccsXLiQnTt34uzsTFhYGDk5OWqdvn37cvDgQdavX8+qVavYunUrQ4YMUdenpaXRpUsXAgMDiYqK4oMPPmDy5MksWrRIrbN9+3aeffZZBg4cyJ49e+jVqxe9evXiwIEDN6/zQgghhBC3sQrdnd2tWze6detW4jqz2cycOXOYMGECjz32GABff/013t7e/PLLL/Tp04fDhw+zZs0a/vnnH1q1agXAJ598Qvfu3fnwww/x8/Nj2bJl5OXlsXjxYvR6PQ0bNiQ6OppZs2apyebcuXPp2rUrY8aMAWDatGmsX7+eefPmsXDhwlvwTAghhBBC3F4q7VS4mJgY4uLi6Ny5s1rm7u5O69atiYyMBCAyMhIPDw81gQTo3LkzGo2GnTt3qnXatWuHXq9X64SFhXH06FGSk5PVOkW3U1incDslyc3NJS0tTV3S09NvvNNCCCGEELeJSptExsXFAeDt7W1V7u3tra6Li4vDy8vLar1Op8PT09OqTkltFN1GaXUK15dkxowZuLu7q0tISIitXRRCCCGEuG1V2iSyshs/fjypqanqcujQoYoOSQghhBDilqm0SaSPjw8A8fHxVuXx8fHqOh8fHxISEqzWGwwGkpKSrOqU1EbRbZRWp3B9Sezt7XFzc1MXV9fKc841IYQQQoibrdImkUFBQfj4+LBhwwa1LC0tjZ07dxIaGgpAaGgoKSkpREVFqXU2btyIyWSidevWap2tW7eSn5+v1lm/fj3169enSpUqap2i2ymsU7gdIYQQQghhrUKTyIyMDKKjo4mOjgYsk2mio6OJjY1FURRGjRrF9OnT+fXXX9m/fz/9+vXDz8+PXr16AdCgQQO6du3K4MGD2bVrF3///TfDhw+nT58++Pn5AfDcc8+h1+sZOHAgBw8e5Pvvv2fu3LlERESocYwcOZI1a9bw0UcfceTIESZPnszu3bsZPnz4rX5KhBBCCCFuCxV6ip/du3fToUMH9X5hYte/f3+WLFnC2LFjyczMZMiQIaSkpPDAAw+wZs0aHBwc1McsW7aM4cOH06lTJzQaDb179+bjjz9W17u7u7Nu3TrCw8Np2bIl1apVY+LEiVbnkrz//vtZvnw5EyZM4M0336Ru3br88ssvNGrU6BY8C0IIIYQQt58KHYls3749ZrO52LJkyRIAFEVh6tSpxMXFkZOTw59//km9evWs2vD09GT58uWkp6eTmprK4sWLcXFxsarTpEkT/vrrL3Jycjh79ixvvPFGsVieeuopjh49Sm5uLgcOHKB79+43rd9CCCGEuD3NmDGDe++9F1dXV7y8vOjVqxdHjx61qpOTk0N4eDhVq1bFxcWF3r17W829SEpK4pFHHsHFxYXmzZuzZ88eq8eHh4fz0Ucf3ZL+3IhKe0ykEEIIIURls2XLFsLDw9mxYwfr168nPz+fLl26kJmZqdYZPXo0v/32GytXrmTLli2cP3+eJ554Ql3/zjvvkJ6ezr///kv79u0ZPHiwum7Hjh3s3LmTUaNG3cpuXZcK3Z0thBBCCHE7WbNmjdX9JUuW4OXlRVRUFO3atSM1NZUvv/yS5cuX07FjRwC++uorGjRowI4dO2jTpg2HDx+mT58+1KtXjyFDhqiXYs7Pz2fo0KF88cUXaLXaW943W0kSWc4MBoM6E1yj0aDVajEajZhMJrVOYbnBYMBsNqvlWq0WjUZTannRGeZgObF64TbLUm5nZ4fJZMJoNKpliqKg0+lKLS8t9rL0yWAwoGC5rZhNwOU+mdGAoqCYL29TLQcUTGUrV7RgNl9RrmBWNCWWA5hMJqvn8so+GQwGNJjAbAJFc5XYb12fNAW3r/Y6FcZt2b7lOSgeo3JL+6TBpL4Pr/beuxw3XH79TOr7p2j5rehTYdz5+fmlfp7UrVu1U/p772b1SYsZO40CCmjsTGRnZ6tX0Cop9uzsbJztzDgqxhLeexowX/m6KpgV5Srl5hL6pKCYzSX0qaRyDdhZdv8VvfLXld972dnZuNqZsFeMBVsp6+fp5vVJsTOTl5dHRkZGqd/lhXE7KPkFr1/h92EZYr9JfdLYmcjJySEnJ6fU7/KcnBw1bjUWhZJfv1vUp8L3d0ZGRpl/nxRFQa/Xo9frr/qbC5Cenk5aWpq63t7eHnt7e64lNTUVsBxeBxAVFUV+fr7VlfCCg4MJCAggMjKSNm3a0LRpUzZu3MigQYNYu3YtTZo0AWDmzJm0b9/e6kp8lZkkkeUsMjISJycnAAICAmjevDn79u0jNjZWrVO/fn2Cg4PZtWsXiYmJanmzZs0IDAxk69atVl+moaGheHl5sW7dOqsfgw4dOuDo6Mjvv/9uFUP37t3Jzs5m06ZNaplOp6NHjx5cvHjR6nKOrq6udOzYkTNnzqiz5AGqV6/O/fffz/Hjx62O9bC1Tx6KO3lU4Z6cGPTmPLX8gr0/2VoXArNPqAkSwBmHWhgUHUHZx6z6FONYD53ZgH/OKbXMhIbTTvVxNGXim3tGLc9T9Jx1rI2rMYXqeZevOpSlcSYDN86cOcPff/991T411EFyvpFkfXW8c8/iZLq8myJR70O67tb2KUdr+ahe63VqqAOy40nTunPR3o+qeXG4GVPV+sm6are2TzrYtesiPXv2LPW9l5CQQENdPGTHq69TnEMAVfIvUcVwUa1/S/ukg8hISzylfZ5CQ0NxUfKsnoOrvfduRp9cXV1xdXXFrNVjRkHnYUdcXJx6tS0HBwcURSE7O9vqdQq9xx6NkofWbJ0YGxQ7FMxW5WYUjIoOBRPaIgmzGQ1GRYsGE5oi5SZFgwktGoxoiiQWJkWLCQ1as3XyalS0mKroiYuL48KFC2q5vb09Wq2W7Oxs9Yf/gXvsMCp5lr6arf+pLin2m90nYxU9ly5dIi4uzuofJL1ej06nIycnB5PJxAP32AE5GBVdibGXVn7T+lTFjoSEBFJTU8nLy7P6XbGzs8POzo6cnBw1bkuMWsxo0JovDw5cLfab0qcqlvd3YmIi9vb25OfnWw0I6HQ69Hp9sT7pdDq8vLyIiYkp8Te38Dv1yivPTZo0icmTJ3M1JpOJUaNG0bZtW3UyblxcHHq9Hg8PD6u6Ra+EN27cOIYNG0bt2rWpWbMmX375JcePH2fp0qVERkYydOhQ1q1bR6tWrfj8889xd3e/ahwVRTEXTcvFdTt79iz+/v7ExMRwzz33ADISmZyczOd/xeDkVqXSjERmpKcx9KFaVh/IK/uUnJzMF3+dwtHVo9KMRGalpzK0Qz08PDxKfZ0uXbrEF3+dwsnVXX0OKnokMis9lUEP1sLLy6vU99jFixdZtOVEQdxw+fWruJHIwrirVKlS6ucpPT2dBZuO4+LqVizGWzES6e+YR4CrQtWq1dDbO4JGwWQ0UMVJr+4GUxTL6HvR7xOj0UhyVh4ajc6q7YIHFBTZUl7CT4hN5Qomk5EqTnZWu+8URUFRFPV7xjruEmIp2NNwK/tkMhnxdLYku0Wf48LYzWYzBoOhIG6tJUaFEp+DW9kny/Otx87OTp3QemXs+fn5ReLm6rHfoj4Vxq3T6dTnt7TnvbDcbDaTnZ1NYmIibm5uVpdKLvxtPX36NEFBQRw6dEj9/YayjUQOGzaMP/74g23btlGjRg0Ali9fzoABA8jNzbWqe99999GhQwfef//9Etvq2LEjI0eO5L///mPVqlWsXr2awYMHU7Vq1Uo7yUZGIsuZTqfDzs7Oqkyr1ZZ4bEPhj1NZy69s93rKNRoNGk3x+VSllZcWe1n6pNNZ/hOFgl0VJTArJR/zYcaGckWxqVyj0ZT43BT2SafTYUIDBTGXHvut65OpIMG52utUGHfR7dsee/n2yYRGfU9c7b13ZdwAKJpiPz/XF7vtfSqMu+j7pOTPmVJyO6W+J8unTxpFwdfJTNVqXji7eajlRqMOR0f7Ur9DwJIMpxs0lep4K6PReNvG7eBwe8Z9redbp9PdlnGXxNnZGUVRSEhIwMfHp1ifCttzdXXFzc2tpCZKNHz4cFatWsXWrVvVBBIsV8HLy8sjJSXFajTyalfC++qrr/Dw8OCxxx7jiSeeoFevXtjZ2fHUU08xceJEG3p7a8nsbCGEuM3oNWa0ioLe3uHalYUQ6mFmV+7Rux5ms5nhw4fz888/s3HjRoKCgqzWt2zZEjs7O6sr4R09epTY2NgSr4SXmJjI1KlT+eSTTwBLslwYZ35+vtUenMpGRiKFEOK2UzCeqShXryaEAC4f2lEewsPDWb58Of/73/9wdXVVj3N0d3fH0dERd3d3Bg4cSEREBJ6enri5uTFixAhCQ0Np06ZNsfZGjRrFa6+9pu5Kb9u2Ld988w1dunRh0aJFtG3bttxiL28yEimEEEIIUUaffvopqamptG/fHl9fX3X5/vvv1TqzZ8+mZ8+e9O7dm3bt2uHj48NPP/1UrK21a9dy4sQJXnnlFbVs+PDh1KpVi9atW5OXl8ekSZNuSb+uh4xECiHEHcJkMpGUlHTNY/SSM3LRasrnWDd3D48Sj3WtrGL/+4/7mgTz5187aNSkaUWHA8DxY0cZOWwwB/fvo069+mzYtrOiQ6JOnToMGPIKQ4e/CoCPuyNfLfuebj0fve42y6ONyqAs85EdHByYP38+8+fPv2q9sLAwwsLCrMqcnJxYsWLFDcV4q0gSKYQQd4jU1BSWbT1SZKZ7cWazicw8I5py2L2Xk5nOkM6NqVJwfryyeHXYYFYs/z/emjSVERFj1PL//e9/PPnkk2X6gb7TfPDuNJycnNm2ey/OV1y2t7LYdywGd48qZar7wYzprFn9W7Fk2JY2xO1BkkghhLiDOLq4Ws3YvpLJZMKcZ0BTyszvW8HBwYF5c2bxwoBBeFS5M5KKvLw89Hr9dT32dEwMncO64h8QWGliupKXd8mzim91G6JyuX32QQghhLgjPNi+I17e3nw864NS60yePJlmzZpZlX3+6XxaNa6v3n912GBefO4p5n44k0Z1AqkX4MNH77+LwWBgyoTxBAf60bxBbb79v6+LtX/i2FF6PtyeQC8PHmrTku3b/rJaf/jQQZ7t/Ri1/KrRqE4gw4e8xKVLl08U/3iPLrw1NoKIiAiqVatWbJdkIZPJxOyZM2jeoDYB1d3p9EBrNv65Tl3v4+7Ivuh/mfX+u/i4O/LBjOkltvN4jy6Mf30U418fRV1/b0KCavD+9ClWI7etGtdn1swZDH95IHVqePH6yHAAdkb+zWNdO1HTuwotQurw9rjXra7znJCQwCOPPIKjoyNBQUEsW7as2PZ93B35Y9Wv6v3z584y9KV+BAf6EeRblS4PteXf3bv4btk3fPTeOxzcvw8fd0d83B35btk3JbZx+OABevfsSk3vKjSoeQ+vvxpOZkaGur7w9V3w8Wya1AuiYe0ARowYYTXDesGCBdStWxcHBwe8vb158sknS3z+xM0hSaQQQohbSqvVMH7iFBYv+pTz587eUFvbtm4hLu48v/yxnsnvvM8H707jhaefwMOjCr9v3Eq/lwYzdtTwYtuZOvFNhg4fyfq/dtDq3tb069ObpKRLAKSmpPDkI91o3KQpazf/zbc//o/EhASG9H/eqo2V3y1Hr9fz999/s3DhwhLj+/jjj/ls/idMmjaDjdv/oX3HzvTv8ySnTp4ALLt46zcIYejwkew7FsMrI0aV2tcV3y5Dp9Pxx8a/mPb+Byyc/zHLln5lVefTT+bQsFFj/ty6g4gx4zl96hTP9n6MHo/2YuP2f/jsq2/YtSOSV199VX3Miy++yJkzZ9i0aRM//PADCxYsICEhodQ4MjMyeLx7F+IunGfpdyvZuG0X4SNHYzKZeOyJJxk6fCT1G4Sw71gM+47F8NgTxRO7zMxM+jzxCB4eHvyxaRufL13G1s0bGT9mtFW9v//ayumYGH5ctYY5Cz7j66+/ZsmSJQDs3r2bV199lalTp3L06FHWrFlDu3btSo1blD/ZnS2EEOKW6/7IYzRs3IQP3p3Ohx9fffLB1XhUqcI7M2eh0WioU7ceC+bOIjs7i5GvjwXg1YgxfDL7Q3ZFbqfXk0+rjxsweCg9H3scgPdnf8ymDetY/vUSho96jcWfL6Rxk6a8OWmqWn/2/IW0CKnLyRPHqV2nLgBBtWrz3nvvXXUi0+zZs3ll5Gh1229PfYe//9rCogXzeO+jOXh5+6DT6XB2cbnm7l6/e2owdcYHKIpCnbr1OHzwIJ8t+ITnX3xJrfNAu4cYViQRjRg+jN5P9WHIKyMAqFW7DtPe+4DePbvy2WefERsbyx9//MGuXbu49957Afjyyy9p0KBBqXH8tPJ7Ll26yJpN29TjYYNq11bXO7u4WC41eJX+/Lzye3Jzcvn4sy9xdnYG4N0PZ9Pvmd68PWU61b28AcvErRkfzkar1VKrdh26d+/Ohg0bGDx4MLGxsTg7O9OzZ09cXV0JDAykefPmV30ORfmSkUghhBAVYsKUd1jx7f9x/OiR626jfnCI1ezwal5eBIc0Uu9rtVqqeHpy8WKi1eNa3Xf5fH06nY6mzVtw/NhRAA7u38fff22hll81dXng3mYAnI65fK37Jk2vnrCkpaVx/vx57m1tfW7A+9qEXlefW957n9X5Dlvd15qYkyesTkbdtFlLq8ccPLCP75d/Y9WX557shclkIiYmhsOHD6PT6WjZ8vLjgoODi133uagD+/fRqElTmyZUXen4sSOENG6sJpAA97UOxWQyceL4cbWsfnCI1RVmfHx81FHShx9+mMDAQGrVqsULL7zAsmXLyMrKuu6YhO1kJFIIIUSFCG37AO07PcyMaZMZ/NKLVus0Gk2xmdqGEq42Ymdn/TOmKEqJZYXX4C6LzMxMunTtzoQp7xRb51XksnWOBVdBqUycnK1jysrM5IUBAxn0crhaZjQZqeqsp1atWhw7dszmbTg43rorJV3ttXR1deXff/9l8+bNrFu3jokTJzJ58mT++eefqybBovzISKQQQogKM2HyNNav+Z0dO3ZYlVevXp24uDirRPLggX3ltt2ofy6ffsZgMLAveg9161km7TRp2oyjRw7jHxhIUO3aVkvRkbNrcXNzw8/Pj392Wvdt145I6gUH2xzzv7v/uaIPuwiqXeeq17du3LQZx44cse5HrdrUqVMHvV5PcHAwBoOBqKgo9TFHjx4lJSWl1DZDGjbm4P59JCcllbheb6e/5qX66tYL5tD+/VYTfHbtjCw4LKHuVR9blE6no3PnzsycOZN9+/Zx+vRpNm7cWObHixsjSaQQQtxBsjPSyUxLKXXJSk8hKz214O+NLTmZ6Tccb4OGjXj8qWeYN2+eVXn79u1JTExk5syZnDx5kgULFrDpz/U3vL1CX33xGb//9j+OHzvK+NdGkZKSwrMv9AdgwOCXSU5OZuhL/dgTtZvTp06x6c/1jHxliM3XMY6IiGDB3Nn88uNKThw/xvRJEzi4fx+Dhw23OeZzZ88w6c2xnDh+jJ9/+J4vF33K4KHhV33M8FGvsXvXDsa/PooD+/Zy6uQJ1v6+Sp1YU79+fbp27crLL7/Mzp07iYqKYtCgQTg6Opba5uNPPo2XlzcD+j7Nrh3b+S8mhlX/+5nduyzJsn9gALH/nebAvr1cunSR3NzcYm088XQf7B3seXXoIA4fOsi2rVt4a0wET/Z5Tj0e8lpWrVrFxx9/THR0NP/99x9ff/01JpOJ+vXrX/vBolzI7mwhhLhDuLt7MKp702tesSaxnK9Yc6PGjJ/Abz//aFXWoEEDFixYwLvvvsu0adN4/PHHeXn4qyz7+qtSWrHNhMnT+GT2hxzcv4+atWqz9NsfqFq1GgA+vn78tm4j0ye+RZ/HHyEvL5ca/gF06PywzVfnGTFiBBcSk5gyYRwXExOpF9yApd/9QK3adWyO+ak+fcnOzqFbxwfRarQMHhrOCwMGXvUxIY0a89Pqdbw3bTKPdeuM2WwmsGYQz/V5Rq3z1VdfMWjQIB566CG8vb2ZPn06b7/9dqlt6vV6vvv5Nya/NY6+Tz2OwWCgXv1gZnw0B4Aejz7O6l//R++eXUlNTWHOgkX06fuCVRtOTk5899NvTHjjdbp1eABHRyd6PNqLKe++X+bnw8PDg59++onJkyeTk5ND3bp1+fbbb2nYsGGZ2xA3RjHfjZcHuAnOnj2Lv78/Z86coUaNGhUdTqWQlJTEgk0nrnri41stMy2FVzrUwfMqB4RL3OVH4r45HDVGmlfJ556AQOz09mq50WjEy9X+mklkQnruVXeB3moS97U93qMLjRo3Ydp7H95QO3BnP9+lycnJISYmhqCgIBwcrI/plN/v6ye7s4UQQgghhM0kiRRCCCGEEDaTYyKFEEKISu7n1euuXUmIW0xGIoUQQgghhM0kiRRCCCGEEDaTJFIIIYQQQthMkkghhBBCCGEzSSKFEEIIIYTNJIkUQgghhBA2kyRSCCGEKODj7sgfq36t6DBu2AczptPpgdYVHYa4w1XqJHLy5MkoimK1BAcHq+tzcnIIDw+natWquLi40Lt3b+Lj463aiI2NpUePHjg5OeHl5cWYMWMwGAxWdTZv3kyLFi2wt7enTp06LFmy5FZ0Twgh7lq7d+3Ar4ozfZ963ObHtmpcn0ULPrkJUd08j/fowtvjXr8pbZeU+L4yYhQrf/39pmxPiEKVOokEaNiwIRcuXFCXbdu2qetGjx7Nb7/9xsqVK9myZQvnz5/niSeeUNcbjUZ69OhBXl4e27dvZ+nSpSxZsoSJEyeqdWJiYujRowcdOnQgOjqaUaNGMWjQINauXXtL+ymEEHeT5V8vZeDLw9ixfRtxFy5UdDjXLS8vr6JDKJGziwuenlUrOgxxh6v0SaROp8PHx0ddqlWrBkBqaipffvkls2bNomPHjrRs2ZKvvvqK7du3s2PHDgDWrVvHoUOH+L//+z+aNWtGt27dmDZtGvPnz1c/+AsXLiQoKIiPPvqIBg0aMHz4cJ588klmz55dYX0WQghbmc1msvOMZOUZrrlk5xnLdTGbzTbFmpmRwf9+/oH+A4fQuUtXVnz7f8Xq/Pbbb9x77704ODhQrVo1nnzyScAyonc2NpaJ48fi4+6Ij7sjUPLu20ULPqFV4/rq/T1Ru3n6sR6EBNWgrr83vbo/zL7oPTbF/niPLox/fRRvj3udRnUC6N69OwAHDhygW7duuLi44O3tzQsvvMDFixcBGBX+MpHb/uLzT+erMcf+9x8Ahw8d5Nnej1HLrxqN6gQyfMhLXLp00Wp7b42NYOrbbxIc6EfjujX5YMZ0dX1h/wb0fQYfd0f1/pXPh8lk4qP336V5g9oE+XjSsmVL1qxZo64/ffo0iqLw008/0aFDB9zc3Oj8YBt279ph0/Mj7i6V/rKHx48fx8/PDwcHB0JDQ5kxYwYBAQFERUWRn59P586d1brBwcEEBAQQGRlJmzZtiIyMpHHjxnh7e6t1wsLCGDZsGAcPHqR58+ZERkZatVFYZ9SoUVeNKzc3l9zcXPV+enp6+XRYCCGuQ06+iU6z/6qQbW9+vT2Oem2Z6//v5x+pU7cederWo/czzzJx3BimTXxLXb969Woef/xx3nrrLb7++mvy8vJYtWoVAIu/+Y5OD9zH8y8O5Pn+A2yKMzMjg6efe553PpiF2Wxm4Sdz6fvU40T+ux8XV9cyt7Pi22X0f2kwv/zxJ1Wd9aSkpNCxY0cGDRrE7Nmzyc7O5o033uDpp59m3bp1TJ0xk5iTJwhu0JCxb70NQNVq1UlNSeHJR7rRt9+LTJ0xk5zsbKZPmsCQ/s/z46o1Vtt7OfxVft+4ld27djJy2GDuax3KQx07sWbTNhrVDmDOgkV07PwwGm3Jr8Pnn85j4by5fDD7Exo0asxvK5fz6KOPcvDgQerWravWe+utt/jwww8JCgpi7Pi3GDqwPzv2HESnq/TpgqgAlfpd0bp1a5YsWUL9+vW5cOECU6ZM4cEHH+TAgQPExcWh1+vx8PCweoy3tzdxcXEAxMXFWSWQhesL112tTlpaGtnZ2Tg6OpYY24wZM5gyZUp5dFMIIe4q336zhCefeRaAjp27MCptCFu3bqVTp04AvPPOO/Tp08fqO7Zhw4YkpOdSxdMTjVaLi4sLXt4+Nm33gYfaW93/8OP51AvwYfvff9Gla/cyt1OrVh0mTnsXo9GIl6s97733Hs2bN+fdd99V6yxevBh/f3+OHTuGh28gdno9jk6OVjEv/nwhjZs05c1JU9Wy2fMX0iKkLidPHKd2HUtyF9KwEa+PsyTZtWrXYfGiT/lryyYe6tiJatWqA+Du7n7V5+PTT+YwfORr9HryaYxGIzNmzGDLli3MmTOH+fPnq/Vef/11evTogcFg4PVxb9Hh/nuJOXWSuvXql9q2uHtV6iSyW7du6u0mTZrQunVrAgMDWbFiRanJ3a0yfvx4IiIi1Pvnzp0jJCSkAiMSQtzNHOw0bBj9INVd9VcdNTIYDCSm56EtZcTqerddVieOH2NP1G4WL/sesByy9OjjvVm8eLGaREZHRzN48OByi69QYkI8702bwvZtW7l4MRGj0Uh2VhbnzpyxqZ0mzZpb3d+7dy+bNm3CxcWlWN1Tp07RwjewxHYO7t/H339toZZftWLrTsecUpPIBg0bW63z9vHl4sXEMsebnpZG3IUL3Nsm1Kq8bdu27N2716qsSZMm6m0vH0tSejExUZJIUaJKnUReycPDg3r16nHixAkefvhh8vLySElJsRqNjI+Px6fgje/j48OuXbus2iicvV20zpUzuuPj43Fzc7tqompvb4+9vb16Py0t7Yb6JoQQN0JRFBz1Wpz0uqsnkRpw1BvLNYm0xfKvl2AwGGhWv5ZaZjabsbe3JzU1FXd39+saJNBoNMWOzczPz7e6/+rQwSQlXWLa+x9Swz8Ae709PR5uT36+bZNjnJydrO5nZGTwyCOP8P777xerW716dTJNJbeTmZlJl67dmTDlnWLrChM4ADs769dTUSzHON4MdnZ2RbajADdvW+L2V+kn1hSVkZHByZMn8fX1pWXLltjZ2bFhwwZ1/dGjR4mNjSU01PLfVmhoKPv37ychIUGts379etzc3NRRw9DQUKs2CusUtiGEEKJ8GAwGVn63nMnvvMef23aqy/qtkfj5+fHtt98CltGwK7+Xi9Lb6TEajVZlVatWIyE+3iqRPLh/n1WdXTsjGTQ0nM5duhLcIAS9vZ6kIpNYrleLFi04ePAgNWvWpE6dOlaLs7NzqTE3adqMo0cO4x8YSFDt2lZL4ePKws7OrljbRbm6ueHj68s/OyKtyv/++2/ZgyZuSKVOIl9//XW2bNnC6dOn2b59O48//jharZZnn30Wd3d3Bg4cSEREBJs2bSIqKooBAwYQGhpKmzZtAOjSpQshISG88MIL7N27l7Vr1zJhwgTCw8PVUcShQ4dy6tQpxo4dy5EjR1iwYAErVqxg9OjRFdl1IYS446xf8zupKck898KLNAhpqC7BIQ15/PHH+fLLLwGYNGkS/9/enUdFXe9/HH8OAwyICLmBCyDaYu5brrkVlR2xa1mWaW5ZqZjiVi4plktqFzP3LbVfZqU3d7My8Zq7IFp6c18SvbilCIhsM/P7g5wbi8sYMjC9HufM4cznu72+H+U7bz7fZb788ksiIiI4dOgQBw4c4KOPPrKtJyAwiF07thP/33O2O5mbNm/B75cvMWNqJKdPnmTh/DlEbfwh2/YrV36Qf321lKNHDhMbs4ewN3rky6VRYWFhXLlyhU6dOhEdHc2JEyf4/vvv6dGjh624CwgMIjYmmjO//cbvv1/GYrHQ4423uHr1Kr17dmXf3hhOnzzJ5h83MqDvm7ctCnMKCAxi65Z/c/HCeRKuXs1znr79BzLjk0hWfbOc48eOMmLECPbv38+AAQP+8v7L31ehLiLPnj1Lp06deOSRR+jYsSOlSpVi165dlCmTdSHxxx9/TGhoKB06dKBFixb4+/uzYsUK2/JGo5F169ZhNBpp0qQJXbp0oWvXrnzwwf8uYg4ODmb9+vVs3LiR2rVrExkZyYIFC3jmmWcKfH9FRJzZ0s8/o3mrJyjh45Nr2vPPP09MTAy//PILrVq1Yvny5axZs4Y6derwxBNPEB0dbZv3nZGjiDvzG43rVKd65QAAHn6kKhMjP2Hxgrk88XhD9u2Noc/b4dm2MWXGbBISrvJ0iyb0e/N1Xn+rL6X++Dz5K8qXL8/27dsxm808/fTT1KxZk/DwcHx9fXFxyfqY7dM/HKPRSMtGdaleOYCzcXH4lyvP2h+isJjNvPJ8O1o3bcDo4UPx8fGxLXc3xoyfyE+bN1Gv2kOEtGic5zy9eofxVlh/3n9vGCGPN+L7779nzZo12e7MFrGXwWrvA74kT2fPniUgIIC4uDgqVqzo6DiFwpUrV5i1+TheJXwdHcXmemICfVs/SMmSJW85j3LnH+W+PzxdzNR9IIMKgUG4uf/v2uybdwvf6caai0lpDrsmMi/KXbCcOfetpKamcurUKYKDg/Hw8Mg2TZ/f965Qj0SKiIiISOGkIlJERERE7KYiUkRERETspiJSREREROymIlJERERE7KYiUkRERETspiJSREREROymIlJERERE7KYiUkRERETspiJSREScUv8+b9D91Zds759v+zSjhg0p8Bw7tv2Ev48n1xISCnzb+c1RfSiFk4pIEREpMP37vIG/jyf+Pp4ElC5B4zrV+Xjyh2RmZt73bS/8/CveHRlxV/Nu31o0Cj9/H082rFuT7+vdsmULFUoWz7X/9vShOD/7v4BSCpzFYiGhEB7IfH19cXHR3yEiYp/WIU/zyay5pKWlsemH7xk+JBzf4p6MHDky17zp6em4u7vny3YfuM13oRc2GRkZuLm5OTpGLkWpD+X+UxFZBCQkJDBl3T48vLwdHcUm9XoSg0LrUlIHFJHCwWrFkJEC6Zlguc2hPTMTQ0Y6WPLxD0DXYmAw3PXsJpM7Zf38Aeje602+XbuatWvXMnLkSLp3705CQgKPPfYYM2fOxGQycerUKeLi4ug/cDA//TsKF4MLjZo2Y+zEfxIYFASA2Wzmg1HD+XLJ/2F0MdLptW5YrdZs232+7dPUqFmLsRP/CUBaWhqTx3/Ayn99zeVLlyhfoSL9Bw3l8Zat6RD6DACPBJUDoOOrXZg2ez4Wi4UZH0fy+eJPuXTxAg899BCjR4/mxRdftG3n22+/JTw8nLi4OBo1asQ/Xup0xz7x9/FkYuQnRP34A1u3bKZv/4EMHf4e361fS+TECRw9cgg//3J0fLUL4UPexdXVlQY1HwGgR+eXAagYGEjMgSMAt10OwM3Njfnz57N+/Xq+//57KlSoQGRkJM899xynT58mJCQkz/3P2YcJV6/y3rAhbNzwLenpaTRu1pzxkyOpXOVBAL764nNGDx/K3IVZP8+dO0ujxk2ZOmsufv7l7vr/jBROKiKLCA8vb7xK+Do6hogUVpkplJv14B1ncwXy+6P7wtsnwc3rnpf38PQk6dpV2/tNmzZRokQJNm7cCGSNyrVt25ba9R5j9YYfMbq6MvWjibza4TmidkTj7u7O7OlT+fqLJXw8Yw4PPVKVOdM/YcO6NTzeouUtt/v2W6+zN3o34yZFUr1GLc78dprff79MhYoV+fTzL3n9tU5s3/sL3t7eeHh4AjAt8iO+WfYlkz+eTlBwML/G7qZLly6UKVOGli1bEhcXxwsvvEBYWBhvvvkmu3fvZsjQoXfVD/+cOJ6RY8Yy9sOPMLoa2bVjG2/37sW4SZE0btKM06dOMmRAGABDho3ku83bqFElkKmz5vFEyFO4GI0Ad1zupvfff5/Jkyfz0UcfMX36dDp37sxvv/1GQEAAy5Yto2PHjrn2P6cBfd/k5InjfPbVcry9SzAu4j06v9ien/bss42k3khJYfb0qUyf+ykuLi6EvdmT998bzqwFi++qX6TwUhEpIiIOYbVa2frvzWyJ+pGwsDBbu5eXFwsWLLCdxl6yZAkWi4V/TptpG0mbOmsejwT6s2PrT7R6MoT5s2fw9qAhtH2uPQCTp07n31Ebb7ntE8ePsWblNyxbtZ4WrZ8AICg42Dbd94GssyylS5fBx9cXyBq5/GTKZJavXk+Dho0xm808VrMqO3fuZO7cubRs2ZLZs2dTpUoVIiMjAahSpQp7Yn9m5idT7tgfL7zUkU5dutreDwzrzdvhQ3j51S62fO++F8HY0SMYMmwkpUuXAcDHx8c2sgsQOXHCbZe7qXv37nTqlDVKOmHCBKZNm8aePXto06aN7SzTn/c/p5MnjvP9t+tY+0MUjzVqAsDMBYuoX+0hNqxbw3PPdwCy/giY/PF0KlWuDEDPN3ozZfKHd+wPKfxURIqIOAPXYsT3PU6Z4u62QisvmZmZXEpOx8WYz6ez7bDxuw1ULl+azIwMLBYL7V/syOjRo23Ta9asme06yJ9//pnjx4/zcKB/tvWkpqZy+tRJEq9d48L589Rr0PB/kVxdqV23Xq5T2jcd/OVnjEYjTR5vfte5T508wY2UFDq2D7W1Gci6brNu3boAHDp0iEaNGmVbrv5jDbkbtevWy/b+PwcPEL17J1MjJ9naLGYzqamppKSkUKxY3v1+p+VMJhMAtWrVsk338vKiRIkSXLx48a6yAhw7chhXV9ds/V6yZCmqPPgwx44esbV5FitmKyAB/Pz9uXzp7rcjhZeKSBERZ2AwYHUrBu4muE0RiUsmVjdX+OPUpyM0a96SSVOm4ebuhn+58hgMBry8TLbpXl7ZT40nJydTr149Pp69AKNL9tylSpe+pwyennmfnr2dlOvJACxZtpJy5cpjtpgp5ZVVtN8szP6KYsWy73fK9WSGDH+Ptu3a55rXw8Pjtjlvt9zNwjrnjTsGgwGLxWJ/8DvIazu3Ku6laFERKSIiBaqYVzGCq1SxvTebzbedv169enz99deULl0G3wceyHMeP39/YmP20KTZ40DWiOsv+/dRs3adPOevWq0GFouFndu22k5n/5m7e1bhY7b8L9vDjzyKyWTi3Nk4mj7eHLPZTFlvU7aR30cffZQ1a7I/cic2Jvq2+3crNWvX4cSxY9n6Kic3N7dc/Xen5e7U3zfXC9n3P6eHHqlKZmYmsTF7bKezr1z5nRPHj/LwI1XvuA0p+vR8FhERKdQ6d+5M6dKl6dHlZXbt2MZvp0+zfetPjHxnEP89dxaAXr3DmPFxJBvWreHY0SMMGzSAa9eu3XKdgUFBdHy1CwP7vcWGdWts61y94l8AVAwIxGAwsPG7DVy+fInryckU9/amz9vhRAx/h6+XLuH0qZPExsYyffp0PvvsMwB69+7NsWPHGDp0KEeOHOHLL79k2ZdL7mm/B70zguVffcE/J47n8KFfOXrkMKv+tYyJY8fY5gkIDGLrln9z8cJ5Eq5evevl7iQoKCjX/udUucqDtGkbyuD+YezeuZ3/HPiFsDd64l+uPG3atrunfZaiRUWkiIgUasWKFSMqKooKFQPo2aUTLRrWYVC/3qSlpuHtXQKAPm+H8+Irnejf5w1CQ1rh5V2cZ0Ofu+16J02ZRug/nmfY4AE0f6w2Q/r3JSUlBYBy5SswdMQoxo8ZRc0Hgxg+dCAA774XwcB3hjF9yke0alyf0NBQ1q9fT/AfN+UEBgbyzTffsGrVKmrXrs28efMY9t6Ye9rv1iFP8fnXK9gS9SPPtn6ctiEtmTtrOhUDAm3zjBk/kZ82b6JetYcIadH4rpe7kwoVKjB42Mhc+5/T1JnzqFWnLq+93IHQp1qB1coX/1pVKJ9xKfnPYNWFCfni7NmzBAQEEBcXR8WKFfN13VeuXGHW5uOF6hE/1xMT6Nv6wds+J1K5849yF6zCntvTxUzdBzKoEBiEm/v/rsXL6/RqTpmZmVxMSsPowGsic1LuguXMuW8lNTWVU6dOERwcnOt60vv5+e3sNBIpIiIiInZTESkiIiIidlMRKSIiIiJ2UxEpIiIiInZTESkiUuQYsn7ovkiRu6J7iO8PFZEiIkVMusWA2WolPS3V0VFEioSbj27So4fyl76xRkSkiDFj4L8pLrhdvgSAu8kDDIY/vh/ZesdHt2Smp2MpRI9uUe6C5cy5c7JaraSkpHDx4kV8fX0L1SOLnIGKSBGRIigu1R1IJ8N8AaMh6/S21WohycMNF5dbn2SyWCwkpWZgMBSeE1HKXbCcOfet+Pr64u/vfx9S/b2piBQRKZIMxKWa+G+qFXcXK2DlxvUkOjX0x9fX95ZLJSQk8MOeM3h6eRdY0jtR7oLlzLnz4ubmphHI+0RFpIhIEWbGwA1L1kjk9Qxwd3fP9Y0cf+bu7s71DMBSeD5UlbtgOXNuKViFZ5y6kJg5cyaVKlXCw8ODRo0asWfPHkdHEhERkULmdvXCoEGDKFmyJAEBAXzxxRfZllu+fDnt2rUr6Lj3hYrIP/n6668ZNGgQERERxMbGUrt2bZ555hkuXrzo6GgiIiJSSNyuXli7di1Lly7lhx9+YPLkyfTq1YvLly8DcO3aNUaOHMnMmTMdvAf5Q0Xkn0yZMoU33niDHj16UK1aNebMmUOxYsVYuHCho6OJiIhIIXG7euHQoUO0atWKBg0a0KlTJ0qUKMGpU6cAeOedd+jTpw+BgYEO3oP8oWsi/5Cens7evXsZPny4rc3FxYWQkBB27tyZa/60tDTS0tJs769duwbA2bNnyczMBMBgMGA0GjGbzdkedOri4oKLi8st228uf1NiYiIX407iWaxYtnbLzeVyZLt1uwGw5tluwHrz8cW3bc+6fN9ARkoyZ864k5iYeMt9SkhI4FLcCa4VK44L1hzrBjDcov3+7dONlBTi4ky23JD73ykhIYHfz54g0dMLax4Zb/ZBQe5T2o3rnDvnSXJyMhaLxdZ+M7vFYuHKlSv8fvYESZ5etoy3+vcrqH1Kv3GdM2fcSUlJwWKxZMsO4Orqyu+//27L/ec+uFX2gtinm7kTExNtF+SbzeZs8yclJXEx7gSexbxyrMf+36f82qfMG9l/L/PKnpCQwMW4E5iKed3XY4Q9+5SakpItN+Q+Ht78vbz2x/8TRxz3cranpmT9XiYlJd3yWH4zd5Knl8OOeznb029cJy7OREpKyi0/hy5fvpzt99IRx72c2W/+XiYlJdmOe3kdD+39zD179iyQ9TleokQJ23STyYTJZCKnO9ULffv2Zd68eVy9epWTJ09y48YNHnzwQbZt20ZsbCyzZs3Ktc4iyypWq9VqPXfunBWw7tixI1v70KFDrQ0bNsw1f0RERNbvlF566aWXXnrp5XSviIiIe64XIiIirFWqVLHWqFHDumLFCmtaWpq1Ro0a1piYGOv06dOtDz/8sLVp06bWgwcP5k8R4yAaibxHw4cPZ9CgQbb3mZmZHDp0iICAgHt6hpXcWlJSEtWqVePXX3/F27vwPG7CWam/C5b6u2CpvwtWUehvi8XCmTNnqFatWrYHmec1Cnm3xowZw5gxY2zv33//fUJCQnBzc2PcuHEcOHCAdevW0bVrV/bu3ftX4juUisg/lC5dGqPRyIULF7K1X7hwIc8HlOY1zN2sWbP7mvHv6uYprgoVKmQ71SD3h/q7YKm/C5b6u2AVlf625xpFe+uFw4cPs2TJEvbt28fChQtp0aIFZcqUoWPHjvTs2ZOkpKRCW2DfiYbM/uDu7k79+vXZtGmTrc1isbBp0yaaNGniwGQiIiJSWNhTL1itVt566y2mTJlC8eLFMZvNZGRkANh+5rzeuijRSOSfDBo0iG7dutGgQQMaNmzI1KlTuX79Oj169HB0NBERESkk7rZeWLBgAWXKlLE9F7JZs2aMGTOGXbt2sWHDBqpVq2b3N/AUJioi/+Tll1/m0qVLjB49mvPnz1OnTh2+++47/Pz8HB3tb81kMhEREfGXrk+Ru6f+Lljq74Kl/i5Yztrfd1MvXLhwgfHjx7Njxw5bW8OGDRk8eDBt27albNmyfPbZZ46In28MVuuf7ncXEREREbkLuiZSREREROymIlJERERE7KYiUkRERETspiJSREREROymIlIKrQ8//JDHHnsMb29vypYtS/v27Tly5IijY/0tTJw4EYPBQHh4uKOjOLVz587RpUsXSpUqhaenJzVr1iQmJsbRsZyS2Wxm1KhRBAcH4+npSZUqVRg7diy6tzR//PTTT7Rr147y5ctjMBhYtWpVtulWq5XRo0dTrlw5PD09CQkJ4dixY44JK/lGRaQUWlu2bCEsLIxdu3axceNGMjIyePrpp7l+/bqjozm16Oho5s6dS61atRwdxaldvXqVZs2a4ebmxoYNG/j111+JjIzkgQcecHQ0pzRp0iRmz57NjBkzOHToEJMmTWLy5MlMnz7d0dGcwvXr16lduzYzZ87Mc/rkyZOZNm0ac+bMYffu3Xh5efHMM8+QmppawEklP+kRP1JkXLp0ibJly7JlyxZatGjh6DhOKTk5mXr16jFr1izGjRtHnTp1mDp1qqNjOaVhw4axfft2tm7d6ugofwuhoaH4+fnx6aef2to6dOiAp6cnS5YscWAy52MwGFi5ciXt27cHskYhy5cvz+DBgxkyZAgA165dw8/Pj8WLF/PKK684MK38FRqJlCLj2rVrAJQsWdLBSZxXWFgYbdu2JSQkxNFRnN6aNWto0KABL730EmXLlqVu3brMnz/f0bGcVtOmTdm0aRNHjx4F4Oeff2bbtm08++yzDk7m/E6dOsX58+ezHVd8fHxo1KgRO3fudGAy+av0jTVSJFgsFsLDw2nWrBk1atRwdByn9NVXXxEbG0t0dLSjo/wtnDx5ktmzZzNo0CBGjBhBdHQ0/fv3x93dnW7dujk6ntMZNmwYiYmJVK1aFaPRiNlsZvz48XTu3NnR0Zze+fPnAXJ9+5ufn59tmhRNKiKlSAgLC+PgwYNs27bN0VGcUlxcHAMGDGDjxo14eHg4Os7fgsVioUGDBkyYMAGAunXrcvDgQebMmaMi8j5YtmwZX3zxBUuXLqV69ers37+f8PBwypcvr/4WuUc6nS2FXr9+/Vi3bh2bN2+mYsWKjo7jlPbu3cvFixepV68erq6uuLq6smXLFqZNm4arqytms9nREZ1OuXLlqFatWra2Rx99lDNnzjgokXMbOnQow4YN45VXXqFmzZq89tprDBw4kA8//NDR0Zyev78/kPVd0n924cIF2zQpmlRESqFltVrp168fK1euJCoqiuDgYEdHclpPPvkkBw4cYP/+/bZXgwYN6Ny5M/v378doNDo6otNp1qxZrkdWHT16lKCgIAclcm4pKSm4uGT/yDMajVgsFgcl+vsIDg7G39+fTZs22doSExPZvXs3TZo0cWAy+at0OlsKrbCwMJYuXcrq1avx9va2XTvj4+ODp6eng9M5F29v71zXmnp5eVGqVCldg3qfDBw4kKZNmzJhwgQ6duzInj17mDdvHvPmzXN0NKfUrl07xo8fT2BgINWrV2ffvn1MmTKFnj17OjqaU0hOTub48eO296dOnWL//v2ULFmSwMBAwsPDGTduHA899BDBwcGMGjWK8uXL2+7gliLKKlJIAXm+Fi1a5OhofwstW7a0DhgwwNExnNratWutNWrUsJpMJmvVqlWt8+bNc3Qkp5WYmGgdMGCANTAw0Orh4WGtXLmydeTIkda0tDRHR3MKmzdvzvN43a1bN6vVarVaLBbrqFGjrH5+flaTyWR98sknrUeOHHFsaPnL9JxIEREREbGbrokUEREREbupiBQRERERu6mIFBERERG7qYgUEREREbupiBQRERERu6mIFBERERG7qYgUEREREbupiBQRp2QwGFi1apXt/eHDh2ncuDEeHh7UqVMnz7bTp09jMBjYv39/vuWoVKkSU6dOzbf1iYgUFioiRaRI6d69OwaDAYPBgJubG35+fjz11FMsXLgw2/cgx8fH8+yzz9reR0RE4OXlxZEjR2zf4ZuzLSAggPj4+Pv6VY85i1sRkaJKRaSIFDlt2rQhPj6e06dPs2HDBlq3bs2AAQMIDQ0lMzMTAH9/f0wmk22ZEydO8PjjjxMUFESpUqXybDMajfj7++Pq6uqQ/RIRKUpURIpIkWMymfD396dChQrUq1ePESNGsHr1ajZs2MDixYuB7CN+BoOBvXv38sEHH2AwGBgzZkyebXmdzv7Pf/5DaGgoJUqUwNvbm+bNm3PixAkAWrVqRXh4eLZs7du3p3v37nnmrlSpEgDPP/88BoOBSpUqcfr0aVxcXIiJick279SpUwkKCso2uioiUpioiBQRp/DEE09Qu3ZtVqxYkWtafHw81atXZ/DgwcTHxzNkyJA823I6d+4cLVq0wGQyERUVxd69e+nZs6dttNNe0dHRACxatIj4+Hiio6OpVKkSISEhLFq0KNu8ixYtonv37ri46DAtIoWTztmIiNOoWrUqv/zyS672m6eoixcvjr+/PwDFixfP1Xb58uVsy82cORMfHx+++uor3NzcAHj44YfvOV+ZMmUA8PX1tW0ToFevXvTu3ZspU6ZgMpmIjY3lwIEDrF69+p63JSJyv+lPXBFxGlarFYPBkG/r279/P82bN7cVkPdL+/btMRqNrFy5EoDFixfTunVr2+lvEZHCSEWkiDiNQ4cOERwcnG/r8/T0vO10FxcXrFZrtraMjAy7t+Pu7k7Xrl1ZtGgR6enpLF26lJ49e9q9HhGRgqQiUkScQlRUFAcOHKBDhw75ts5atWqxdevWWxaGZcqUIT4+3vbebDZz8ODB267Tzc0Ns9mcq71Xr178+OOPzJo1i8zMTF544YW/Fl5E5D5TESkiRU5aWhrnz5/n3LlzxMbGMmHCBP7xj38QGhpK165d8207/fr1IzExkVdeeYWYmBiOHTvG559/zpEjR4Csm3nWr1/P+vXrOXz4MH369CEhIeG266xUqRKbNm3i/PnzXL161db+6KOP0rhxY9599106dep0x1FQERFHUxEpIkXOd999R7ly5ahUqRJt2rRh8+bNTJs2jdWrV2M0GvNtO6VKlSIqKork5GRatmxJ/fr1mT9/vu0ayZ49e9KtWze6du1Ky5YtqVy5Mq1bt77tOiMjI9m4cSMBAQHUrVs327TXX3+d9PR0ncoWkSLBYM15QY+IiDjE2LFjWb58eZ53mIuIFDYaiRQRcbDk5GQOHjzIjBkzePvttx0dR0TkrqiIFBFxsH79+lG/fn1atWqlU9kiUmTodLaIiIiI2E0jkSIiIiJiNxWRIiIiImI3FZEiIiIiYjcVkSIiIiJiNxWRIiIiImI3FZEiIiIiYjcVkSIiIiJiNxWRIiIiImI3FZEiIiIiYrf/BzJKUA5QYNpgAAAAAElFTkSuQmCC",
      "text/plain": [
       "<Figure size 640x480 with 2 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "fig = plt.figure(1)\n",
    "ax1 = fig.add_subplot(111)\n",
    "ax2 = ax1.twinx()\n",
    "lns = []\n",
    "\n",
    "difficulty_calibration = pd.DataFrame(columns=['difficulty', 'predicted_retention', 'actual_retention'])\n",
    "difficulty_calibration = dataset[['difficulty', 'p', 'y']].copy()\n",
    "difficulty_calibration['bin'] = difficulty_calibration['difficulty'].map(round)\n",
    "difficulty_group = difficulty_calibration.groupby('bin').count()\n",
    "\n",
    "lns1 = ax1.bar(x=difficulty_group.index, height=difficulty_group['y'],\n",
    "                ec='k', lw=.2, label='Number of predictions', alpha=0.5)\n",
    "ax1.set_ylabel(\"Number of predictions\")\n",
    "ax1.set_xlabel(\"Difficulty\")\n",
    "lns.append(lns1)\n",
    "\n",
    "difficulty_group = difficulty_calibration.groupby(by='bin').agg('mean')\n",
    "lns2 = ax2.plot(difficulty_group['y'], label='Actual retention')\n",
    "lns3 = ax2.plot(difficulty_group['p'], label='Predicted retention')\n",
    "ax2.set_ylabel(\"Retention\")\n",
    "ax2.set_ylim(0, 1)\n",
    "lns.append(lns2[0])\n",
    "lns.append(lns3[0])\n",
    "\n",
    "labs = [l.get_label() for l in lns]\n",
    "ax2.legend(lns, labs, loc='lower right')\n",
    "plt.grid(linestyle='--')\n",
    "plt.gca().yaxis.set_major_formatter(ticker.FuncFormatter(to_percent))\n",
    "plt.gca().xaxis.set_major_formatter(ticker.FormatStrFormatter('%d'))\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<style type=\"text/css\">\n",
       "#T_bfe4c_row0_col0, #T_bfe4c_row0_col1, #T_bfe4c_row0_col2, #T_bfe4c_row0_col3, #T_bfe4c_row0_col4, #T_bfe4c_row0_col5, #T_bfe4c_row0_col6, #T_bfe4c_row0_col7, #T_bfe4c_row0_col8, #T_bfe4c_row1_col0, #T_bfe4c_row1_col1, #T_bfe4c_row1_col2, #T_bfe4c_row1_col3, #T_bfe4c_row1_col4, #T_bfe4c_row1_col5, #T_bfe4c_row1_col6, #T_bfe4c_row1_col7, #T_bfe4c_row1_col8, #T_bfe4c_row2_col0, #T_bfe4c_row2_col1, #T_bfe4c_row2_col2, #T_bfe4c_row2_col3, #T_bfe4c_row2_col4, #T_bfe4c_row2_col5, #T_bfe4c_row2_col6, #T_bfe4c_row2_col7, #T_bfe4c_row2_col8, #T_bfe4c_row3_col0, #T_bfe4c_row3_col1, #T_bfe4c_row3_col2, #T_bfe4c_row3_col3, #T_bfe4c_row3_col4, #T_bfe4c_row3_col5, #T_bfe4c_row3_col6, #T_bfe4c_row3_col7, #T_bfe4c_row3_col8, #T_bfe4c_row4_col0, #T_bfe4c_row4_col1, #T_bfe4c_row4_col2, #T_bfe4c_row4_col3, #T_bfe4c_row4_col4, #T_bfe4c_row4_col5, #T_bfe4c_row4_col6, #T_bfe4c_row4_col7, #T_bfe4c_row4_col8, #T_bfe4c_row5_col0, #T_bfe4c_row5_col1, #T_bfe4c_row5_col2, #T_bfe4c_row5_col3, #T_bfe4c_row5_col6, #T_bfe4c_row6_col0, #T_bfe4c_row6_col1, #T_bfe4c_row6_col2, #T_bfe4c_row6_col3, #T_bfe4c_row6_col4, #T_bfe4c_row6_col6, #T_bfe4c_row7_col0, #T_bfe4c_row7_col1, #T_bfe4c_row7_col2, #T_bfe4c_row7_col3, #T_bfe4c_row7_col4, #T_bfe4c_row8_col0, #T_bfe4c_row8_col1, #T_bfe4c_row8_col2, #T_bfe4c_row8_col3, #T_bfe4c_row8_col6, #T_bfe4c_row9_col0, #T_bfe4c_row9_col1, #T_bfe4c_row9_col2, #T_bfe4c_row9_col4, #T_bfe4c_row10_col0, #T_bfe4c_row10_col2, #T_bfe4c_row11_col0, #T_bfe4c_row11_col1, #T_bfe4c_row12_col0, #T_bfe4c_row13_col0, #T_bfe4c_row14_col0, #T_bfe4c_row15_col9, #T_bfe4c_row16_col0, #T_bfe4c_row16_col8, #T_bfe4c_row16_col9, #T_bfe4c_row17_col7, #T_bfe4c_row17_col8, #T_bfe4c_row17_col9, #T_bfe4c_row18_col5, #T_bfe4c_row18_col6, #T_bfe4c_row18_col7, #T_bfe4c_row18_col8, #T_bfe4c_row18_col9, #T_bfe4c_row19_col0, #T_bfe4c_row19_col1, #T_bfe4c_row19_col4, #T_bfe4c_row19_col5, #T_bfe4c_row19_col6, #T_bfe4c_row19_col7, #T_bfe4c_row19_col8, #T_bfe4c_row19_col9 {\n",
       "  background-color: #000000;\n",
       "  color: #f1f1f1;\n",
       "}\n",
       "#T_bfe4c_row0_col9 {\n",
       "  background-color: #2121ff;\n",
       "  color: #f1f1f1;\n",
       "}\n",
       "#T_bfe4c_row1_col9 {\n",
       "  background-color: #2d2dff;\n",
       "  color: #f1f1f1;\n",
       "}\n",
       "#T_bfe4c_row2_col9 {\n",
       "  background-color: #2929ff;\n",
       "  color: #f1f1f1;\n",
       "}\n",
       "#T_bfe4c_row3_col9 {\n",
       "  background-color: #5d5dff;\n",
       "  color: #f1f1f1;\n",
       "}\n",
       "#T_bfe4c_row4_col9, #T_bfe4c_row6_col5 {\n",
       "  background-color: #b5b5ff;\n",
       "  color: #000000;\n",
       "}\n",
       "#T_bfe4c_row5_col4, #T_bfe4c_row7_col6, #T_bfe4c_row10_col1, #T_bfe4c_row10_col5 {\n",
       "  background-color: #e5e5ff;\n",
       "  color: #000000;\n",
       "}\n",
       "#T_bfe4c_row5_col5, #T_bfe4c_row7_col9 {\n",
       "  background-color: #d9d9ff;\n",
       "  color: #000000;\n",
       "}\n",
       "#T_bfe4c_row5_col7 {\n",
       "  background-color: #ff4949;\n",
       "  color: #f1f1f1;\n",
       "}\n",
       "#T_bfe4c_row5_col8, #T_bfe4c_row16_col2, #T_bfe4c_row17_col6 {\n",
       "  background-color: #cdcdff;\n",
       "  color: #000000;\n",
       "}\n",
       "#T_bfe4c_row5_col9 {\n",
       "  background-color: #7171ff;\n",
       "  color: #f1f1f1;\n",
       "}\n",
       "#T_bfe4c_row6_col7, #T_bfe4c_row15_col6 {\n",
       "  background-color: #fff9f9;\n",
       "  color: #000000;\n",
       "}\n",
       "#T_bfe4c_row6_col8, #T_bfe4c_row12_col3 {\n",
       "  background-color: #f5f5ff;\n",
       "  color: #000000;\n",
       "}\n",
       "#T_bfe4c_row6_col9, #T_bfe4c_row18_col2 {\n",
       "  background-color: #8d8dff;\n",
       "  color: #f1f1f1;\n",
       "}\n",
       "#T_bfe4c_row7_col5, #T_bfe4c_row8_col4, #T_bfe4c_row12_col1, #T_bfe4c_row17_col3 {\n",
       "  background-color: #a9a9ff;\n",
       "  color: #000000;\n",
       "}\n",
       "#T_bfe4c_row7_col7 {\n",
       "  background-color: #9191ff;\n",
       "  color: #f1f1f1;\n",
       "}\n",
       "#T_bfe4c_row7_col8, #T_bfe4c_row8_col7, #T_bfe4c_row10_col7 {\n",
       "  background-color: #d5d5ff;\n",
       "  color: #000000;\n",
       "}\n",
       "#T_bfe4c_row8_col5, #T_bfe4c_row10_col3, #T_bfe4c_row13_col3 {\n",
       "  background-color: #e1e1ff;\n",
       "  color: #000000;\n",
       "}\n",
       "#T_bfe4c_row8_col8, #T_bfe4c_row9_col6, #T_bfe4c_row16_col3 {\n",
       "  background-color: #f1f1ff;\n",
       "  color: #000000;\n",
       "}\n",
       "#T_bfe4c_row8_col9, #T_bfe4c_row9_col8 {\n",
       "  background-color: #ffd9d9;\n",
       "  color: #000000;\n",
       "}\n",
       "#T_bfe4c_row9_col3, #T_bfe4c_row11_col4 {\n",
       "  background-color: #ffeded;\n",
       "  color: #000000;\n",
       "}\n",
       "#T_bfe4c_row9_col5 {\n",
       "  background-color: #ddddff;\n",
       "  color: #000000;\n",
       "}\n",
       "#T_bfe4c_row9_col7, #T_bfe4c_row11_col2 {\n",
       "  background-color: #e9e9ff;\n",
       "  color: #000000;\n",
       "}\n",
       "#T_bfe4c_row9_col9, #T_bfe4c_row14_col5 {\n",
       "  background-color: #ffa1a1;\n",
       "  color: #000000;\n",
       "}\n",
       "#T_bfe4c_row10_col4, #T_bfe4c_row11_col5, #T_bfe4c_row11_col7 {\n",
       "  background-color: #ffdddd;\n",
       "  color: #000000;\n",
       "}\n",
       "#T_bfe4c_row10_col6, #T_bfe4c_row15_col3, #T_bfe4c_row18_col4 {\n",
       "  background-color: #ededff;\n",
       "  color: #000000;\n",
       "}\n",
       "#T_bfe4c_row10_col8, #T_bfe4c_row16_col5 {\n",
       "  background-color: #ffc1c1;\n",
       "  color: #000000;\n",
       "}\n",
       "#T_bfe4c_row10_col9 {\n",
       "  background-color: #ff6969;\n",
       "  color: #f1f1f1;\n",
       "}\n",
       "#T_bfe4c_row11_col3, #T_bfe4c_row17_col2 {\n",
       "  background-color: #d1d1ff;\n",
       "  color: #000000;\n",
       "}\n",
       "#T_bfe4c_row11_col6 {\n",
       "  background-color: #fdfdff;\n",
       "  color: #000000;\n",
       "}\n",
       "#T_bfe4c_row11_col8, #T_bfe4c_row14_col6, #T_bfe4c_row14_col8 {\n",
       "  background-color: #ff9595;\n",
       "  color: #000000;\n",
       "}\n",
       "#T_bfe4c_row11_col9 {\n",
       "  background-color: #ff7d7d;\n",
       "  color: #f1f1f1;\n",
       "}\n",
       "#T_bfe4c_row12_col2 {\n",
       "  background-color: #b9b9ff;\n",
       "  color: #000000;\n",
       "}\n",
       "#T_bfe4c_row12_col4, #T_bfe4c_row12_col7 {\n",
       "  background-color: #fff1f1;\n",
       "  color: #000000;\n",
       "}\n",
       "#T_bfe4c_row12_col5 {\n",
       "  background-color: #ffd1d1;\n",
       "  color: #000000;\n",
       "}\n",
       "#T_bfe4c_row12_col6 {\n",
       "  background-color: #ffc9c9;\n",
       "  color: #000000;\n",
       "}\n",
       "#T_bfe4c_row12_col8 {\n",
       "  background-color: #ff9999;\n",
       "  color: #000000;\n",
       "}\n",
       "#T_bfe4c_row12_col9, #T_bfe4c_row15_col4 {\n",
       "  background-color: #ffb1b1;\n",
       "  color: #000000;\n",
       "}\n",
       "#T_bfe4c_row13_col1, #T_bfe4c_row17_col1 {\n",
       "  background-color: #8181ff;\n",
       "  color: #f1f1f1;\n",
       "}\n",
       "#T_bfe4c_row13_col2, #T_bfe4c_row18_col3 {\n",
       "  background-color: #bdbdff;\n",
       "  color: #000000;\n",
       "}\n",
       "#T_bfe4c_row13_col4, #T_bfe4c_row14_col4 {\n",
       "  background-color: #ffd5d5;\n",
       "  color: #000000;\n",
       "}\n",
       "#T_bfe4c_row13_col5, #T_bfe4c_row15_col8 {\n",
       "  background-color: #ffa9a9;\n",
       "  color: #000000;\n",
       "}\n",
       "#T_bfe4c_row13_col6, #T_bfe4c_row14_col7, #T_bfe4c_row17_col5 {\n",
       "  background-color: #ffbdbd;\n",
       "  color: #000000;\n",
       "}\n",
       "#T_bfe4c_row13_col7, #T_bfe4c_row13_col8 {\n",
       "  background-color: #ff9191;\n",
       "  color: #000000;\n",
       "}\n",
       "#T_bfe4c_row13_col9 {\n",
       "  background-color: #ff8989;\n",
       "  color: #000000;\n",
       "}\n",
       "#T_bfe4c_row14_col1 {\n",
       "  background-color: #9595ff;\n",
       "  color: #f1f1f1;\n",
       "}\n",
       "#T_bfe4c_row14_col2 {\n",
       "  background-color: #c1c1ff;\n",
       "  color: #000000;\n",
       "}\n",
       "#T_bfe4c_row14_col3, #T_bfe4c_row16_col6 {\n",
       "  background-color: #fff5f5;\n",
       "  color: #000000;\n",
       "}\n",
       "#T_bfe4c_row14_col9 {\n",
       "  background-color: #ff7575;\n",
       "  color: #f1f1f1;\n",
       "}\n",
       "#T_bfe4c_row15_col0 {\n",
       "  background-color: #6d6dff;\n",
       "  color: #f1f1f1;\n",
       "}\n",
       "#T_bfe4c_row15_col1, #T_bfe4c_row19_col2 {\n",
       "  background-color: #b1b1ff;\n",
       "  color: #000000;\n",
       "}\n",
       "#T_bfe4c_row15_col2 {\n",
       "  background-color: #c5c5ff;\n",
       "  color: #000000;\n",
       "}\n",
       "#T_bfe4c_row15_col5 {\n",
       "  background-color: #ffe1e1;\n",
       "  color: #000000;\n",
       "}\n",
       "#T_bfe4c_row15_col7, #T_bfe4c_row16_col7 {\n",
       "  background-color: #ff8585;\n",
       "  color: #f1f1f1;\n",
       "}\n",
       "#T_bfe4c_row16_col1 {\n",
       "  background-color: #a1a1ff;\n",
       "  color: #f1f1f1;\n",
       "}\n",
       "#T_bfe4c_row16_col4 {\n",
       "  background-color: #ffa5a5;\n",
       "  color: #000000;\n",
       "}\n",
       "#T_bfe4c_row17_col0 {\n",
       "  background-color: #4141ff;\n",
       "  color: #f1f1f1;\n",
       "}\n",
       "#T_bfe4c_row17_col4 {\n",
       "  background-color: #ffc5c5;\n",
       "  color: #000000;\n",
       "}\n",
       "#T_bfe4c_row18_col0 {\n",
       "  background-color: #0000fd;\n",
       "  color: #f1f1f1;\n",
       "}\n",
       "#T_bfe4c_row18_col1 {\n",
       "  background-color: #9999ff;\n",
       "  color: #f1f1f1;\n",
       "}\n",
       "#T_bfe4c_row19_col3 {\n",
       "  background-color: #c9c9ff;\n",
       "  color: #000000;\n",
       "}\n",
       "</style>\n",
       "<table id=\"T_bfe4c\">\n",
       "  <thead>\n",
       "    <tr>\n",
       "      <th class=\"index_name level0\" >d_bin</th>\n",
       "      <th id=\"T_bfe4c_level0_col0\" class=\"col_heading level0 col0\" >1</th>\n",
       "      <th id=\"T_bfe4c_level0_col1\" class=\"col_heading level0 col1\" >2</th>\n",
       "      <th id=\"T_bfe4c_level0_col2\" class=\"col_heading level0 col2\" >3</th>\n",
       "      <th id=\"T_bfe4c_level0_col3\" class=\"col_heading level0 col3\" >4</th>\n",
       "      <th id=\"T_bfe4c_level0_col4\" class=\"col_heading level0 col4\" >5</th>\n",
       "      <th id=\"T_bfe4c_level0_col5\" class=\"col_heading level0 col5\" >6</th>\n",
       "      <th id=\"T_bfe4c_level0_col6\" class=\"col_heading level0 col6\" >7</th>\n",
       "      <th id=\"T_bfe4c_level0_col7\" class=\"col_heading level0 col7\" >8</th>\n",
       "      <th id=\"T_bfe4c_level0_col8\" class=\"col_heading level0 col8\" >9</th>\n",
       "      <th id=\"T_bfe4c_level0_col9\" class=\"col_heading level0 col9\" >10</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th class=\"index_name level0\" >s_bin</th>\n",
       "      <th class=\"blank col0\" >&nbsp;</th>\n",
       "      <th class=\"blank col1\" >&nbsp;</th>\n",
       "      <th class=\"blank col2\" >&nbsp;</th>\n",
       "      <th class=\"blank col3\" >&nbsp;</th>\n",
       "      <th class=\"blank col4\" >&nbsp;</th>\n",
       "      <th class=\"blank col5\" >&nbsp;</th>\n",
       "      <th class=\"blank col6\" >&nbsp;</th>\n",
       "      <th class=\"blank col7\" >&nbsp;</th>\n",
       "      <th class=\"blank col8\" >&nbsp;</th>\n",
       "      <th class=\"blank col9\" >&nbsp;</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th id=\"T_bfe4c_level0_row0\" class=\"row_heading level0 row0\" >0.260000</th>\n",
       "      <td id=\"T_bfe4c_row0_col0\" class=\"data row0 col0\" ></td>\n",
       "      <td id=\"T_bfe4c_row0_col1\" class=\"data row0 col1\" ></td>\n",
       "      <td id=\"T_bfe4c_row0_col2\" class=\"data row0 col2\" ></td>\n",
       "      <td id=\"T_bfe4c_row0_col3\" class=\"data row0 col3\" ></td>\n",
       "      <td id=\"T_bfe4c_row0_col4\" class=\"data row0 col4\" ></td>\n",
       "      <td id=\"T_bfe4c_row0_col5\" class=\"data row0 col5\" ></td>\n",
       "      <td id=\"T_bfe4c_row0_col6\" class=\"data row0 col6\" ></td>\n",
       "      <td id=\"T_bfe4c_row0_col7\" class=\"data row0 col7\" ></td>\n",
       "      <td id=\"T_bfe4c_row0_col8\" class=\"data row0 col8\" ></td>\n",
       "      <td id=\"T_bfe4c_row0_col9\" class=\"data row0 col9\" >-8.60%</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th id=\"T_bfe4c_level0_row1\" class=\"row_heading level0 row1\" >0.360000</th>\n",
       "      <td id=\"T_bfe4c_row1_col0\" class=\"data row1 col0\" ></td>\n",
       "      <td id=\"T_bfe4c_row1_col1\" class=\"data row1 col1\" ></td>\n",
       "      <td id=\"T_bfe4c_row1_col2\" class=\"data row1 col2\" ></td>\n",
       "      <td id=\"T_bfe4c_row1_col3\" class=\"data row1 col3\" ></td>\n",
       "      <td id=\"T_bfe4c_row1_col4\" class=\"data row1 col4\" ></td>\n",
       "      <td id=\"T_bfe4c_row1_col5\" class=\"data row1 col5\" ></td>\n",
       "      <td id=\"T_bfe4c_row1_col6\" class=\"data row1 col6\" ></td>\n",
       "      <td id=\"T_bfe4c_row1_col7\" class=\"data row1 col7\" ></td>\n",
       "      <td id=\"T_bfe4c_row1_col8\" class=\"data row1 col8\" ></td>\n",
       "      <td id=\"T_bfe4c_row1_col9\" class=\"data row1 col9\" >-8.20%</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th id=\"T_bfe4c_level0_row2\" class=\"row_heading level0 row2\" >0.510000</th>\n",
       "      <td id=\"T_bfe4c_row2_col0\" class=\"data row2 col0\" ></td>\n",
       "      <td id=\"T_bfe4c_row2_col1\" class=\"data row2 col1\" ></td>\n",
       "      <td id=\"T_bfe4c_row2_col2\" class=\"data row2 col2\" ></td>\n",
       "      <td id=\"T_bfe4c_row2_col3\" class=\"data row2 col3\" ></td>\n",
       "      <td id=\"T_bfe4c_row2_col4\" class=\"data row2 col4\" ></td>\n",
       "      <td id=\"T_bfe4c_row2_col5\" class=\"data row2 col5\" ></td>\n",
       "      <td id=\"T_bfe4c_row2_col6\" class=\"data row2 col6\" ></td>\n",
       "      <td id=\"T_bfe4c_row2_col7\" class=\"data row2 col7\" ></td>\n",
       "      <td id=\"T_bfe4c_row2_col8\" class=\"data row2 col8\" ></td>\n",
       "      <td id=\"T_bfe4c_row2_col9\" class=\"data row2 col9\" >-8.38%</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th id=\"T_bfe4c_level0_row3\" class=\"row_heading level0 row3\" >0.710000</th>\n",
       "      <td id=\"T_bfe4c_row3_col0\" class=\"data row3 col0\" ></td>\n",
       "      <td id=\"T_bfe4c_row3_col1\" class=\"data row3 col1\" ></td>\n",
       "      <td id=\"T_bfe4c_row3_col2\" class=\"data row3 col2\" ></td>\n",
       "      <td id=\"T_bfe4c_row3_col3\" class=\"data row3 col3\" ></td>\n",
       "      <td id=\"T_bfe4c_row3_col4\" class=\"data row3 col4\" ></td>\n",
       "      <td id=\"T_bfe4c_row3_col5\" class=\"data row3 col5\" ></td>\n",
       "      <td id=\"T_bfe4c_row3_col6\" class=\"data row3 col6\" ></td>\n",
       "      <td id=\"T_bfe4c_row3_col7\" class=\"data row3 col7\" ></td>\n",
       "      <td id=\"T_bfe4c_row3_col8\" class=\"data row3 col8\" ></td>\n",
       "      <td id=\"T_bfe4c_row3_col9\" class=\"data row3 col9\" >-6.33%</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th id=\"T_bfe4c_level0_row4\" class=\"row_heading level0 row4\" >1.000000</th>\n",
       "      <td id=\"T_bfe4c_row4_col0\" class=\"data row4 col0\" ></td>\n",
       "      <td id=\"T_bfe4c_row4_col1\" class=\"data row4 col1\" ></td>\n",
       "      <td id=\"T_bfe4c_row4_col2\" class=\"data row4 col2\" ></td>\n",
       "      <td id=\"T_bfe4c_row4_col3\" class=\"data row4 col3\" ></td>\n",
       "      <td id=\"T_bfe4c_row4_col4\" class=\"data row4 col4\" ></td>\n",
       "      <td id=\"T_bfe4c_row4_col5\" class=\"data row4 col5\" ></td>\n",
       "      <td id=\"T_bfe4c_row4_col6\" class=\"data row4 col6\" ></td>\n",
       "      <td id=\"T_bfe4c_row4_col7\" class=\"data row4 col7\" ></td>\n",
       "      <td id=\"T_bfe4c_row4_col8\" class=\"data row4 col8\" ></td>\n",
       "      <td id=\"T_bfe4c_row4_col9\" class=\"data row4 col9\" >-2.84%</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th id=\"T_bfe4c_level0_row5\" class=\"row_heading level0 row5\" >1.400000</th>\n",
       "      <td id=\"T_bfe4c_row5_col0\" class=\"data row5 col0\" ></td>\n",
       "      <td id=\"T_bfe4c_row5_col1\" class=\"data row5 col1\" ></td>\n",
       "      <td id=\"T_bfe4c_row5_col2\" class=\"data row5 col2\" ></td>\n",
       "      <td id=\"T_bfe4c_row5_col3\" class=\"data row5 col3\" ></td>\n",
       "      <td id=\"T_bfe4c_row5_col4\" class=\"data row5 col4\" >-1.09%</td>\n",
       "      <td id=\"T_bfe4c_row5_col5\" class=\"data row5 col5\" >-1.41%</td>\n",
       "      <td id=\"T_bfe4c_row5_col6\" class=\"data row5 col6\" ></td>\n",
       "      <td id=\"T_bfe4c_row5_col7\" class=\"data row5 col7\" >7.16%</td>\n",
       "      <td id=\"T_bfe4c_row5_col8\" class=\"data row5 col8\" >-1.90%</td>\n",
       "      <td id=\"T_bfe4c_row5_col9\" class=\"data row5 col9\" >-5.52%</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th id=\"T_bfe4c_level0_row6\" class=\"row_heading level0 row6\" >1.960000</th>\n",
       "      <td id=\"T_bfe4c_row6_col0\" class=\"data row6 col0\" ></td>\n",
       "      <td id=\"T_bfe4c_row6_col1\" class=\"data row6 col1\" ></td>\n",
       "      <td id=\"T_bfe4c_row6_col2\" class=\"data row6 col2\" ></td>\n",
       "      <td id=\"T_bfe4c_row6_col3\" class=\"data row6 col3\" ></td>\n",
       "      <td id=\"T_bfe4c_row6_col4\" class=\"data row6 col4\" ></td>\n",
       "      <td id=\"T_bfe4c_row6_col5\" class=\"data row6 col5\" >-2.93%</td>\n",
       "      <td id=\"T_bfe4c_row6_col6\" class=\"data row6 col6\" ></td>\n",
       "      <td id=\"T_bfe4c_row6_col7\" class=\"data row6 col7\" >0.28%</td>\n",
       "      <td id=\"T_bfe4c_row6_col8\" class=\"data row6 col8\" >-0.31%</td>\n",
       "      <td id=\"T_bfe4c_row6_col9\" class=\"data row6 col9\" >-4.45%</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th id=\"T_bfe4c_level0_row7\" class=\"row_heading level0 row7\" >2.740000</th>\n",
       "      <td id=\"T_bfe4c_row7_col0\" class=\"data row7 col0\" ></td>\n",
       "      <td id=\"T_bfe4c_row7_col1\" class=\"data row7 col1\" ></td>\n",
       "      <td id=\"T_bfe4c_row7_col2\" class=\"data row7 col2\" ></td>\n",
       "      <td id=\"T_bfe4c_row7_col3\" class=\"data row7 col3\" ></td>\n",
       "      <td id=\"T_bfe4c_row7_col4\" class=\"data row7 col4\" ></td>\n",
       "      <td id=\"T_bfe4c_row7_col5\" class=\"data row7 col5\" >-3.43%</td>\n",
       "      <td id=\"T_bfe4c_row7_col6\" class=\"data row7 col6\" >-0.95%</td>\n",
       "      <td id=\"T_bfe4c_row7_col7\" class=\"data row7 col7\" >-4.22%</td>\n",
       "      <td id=\"T_bfe4c_row7_col8\" class=\"data row7 col8\" >-1.67%</td>\n",
       "      <td id=\"T_bfe4c_row7_col9\" class=\"data row7 col9\" >-1.50%</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th id=\"T_bfe4c_level0_row8\" class=\"row_heading level0 row8\" >3.840000</th>\n",
       "      <td id=\"T_bfe4c_row8_col0\" class=\"data row8 col0\" ></td>\n",
       "      <td id=\"T_bfe4c_row8_col1\" class=\"data row8 col1\" ></td>\n",
       "      <td id=\"T_bfe4c_row8_col2\" class=\"data row8 col2\" ></td>\n",
       "      <td id=\"T_bfe4c_row8_col3\" class=\"data row8 col3\" ></td>\n",
       "      <td id=\"T_bfe4c_row8_col4\" class=\"data row8 col4\" >-3.30%</td>\n",
       "      <td id=\"T_bfe4c_row8_col5\" class=\"data row8 col5\" >-1.23%</td>\n",
       "      <td id=\"T_bfe4c_row8_col6\" class=\"data row8 col6\" ></td>\n",
       "      <td id=\"T_bfe4c_row8_col7\" class=\"data row8 col7\" >-1.57%</td>\n",
       "      <td id=\"T_bfe4c_row8_col8\" class=\"data row8 col8\" >-0.48%</td>\n",
       "      <td id=\"T_bfe4c_row8_col9\" class=\"data row8 col9\" >1.56%</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th id=\"T_bfe4c_level0_row9\" class=\"row_heading level0 row9\" >5.380000</th>\n",
       "      <td id=\"T_bfe4c_row9_col0\" class=\"data row9 col0\" ></td>\n",
       "      <td id=\"T_bfe4c_row9_col1\" class=\"data row9 col1\" ></td>\n",
       "      <td id=\"T_bfe4c_row9_col2\" class=\"data row9 col2\" ></td>\n",
       "      <td id=\"T_bfe4c_row9_col3\" class=\"data row9 col3\" >0.76%</td>\n",
       "      <td id=\"T_bfe4c_row9_col4\" class=\"data row9 col4\" ></td>\n",
       "      <td id=\"T_bfe4c_row9_col5\" class=\"data row9 col5\" >-1.27%</td>\n",
       "      <td id=\"T_bfe4c_row9_col6\" class=\"data row9 col6\" >-0.49%</td>\n",
       "      <td id=\"T_bfe4c_row9_col7\" class=\"data row9 col7\" >-0.80%</td>\n",
       "      <td id=\"T_bfe4c_row9_col8\" class=\"data row9 col8\" >1.54%</td>\n",
       "      <td id=\"T_bfe4c_row9_col9\" class=\"data row9 col9\" >3.60%</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th id=\"T_bfe4c_level0_row10\" class=\"row_heading level0 row10\" >7.530000</th>\n",
       "      <td id=\"T_bfe4c_row10_col0\" class=\"data row10 col0\" ></td>\n",
       "      <td id=\"T_bfe4c_row10_col1\" class=\"data row10 col1\" >-1.09%</td>\n",
       "      <td id=\"T_bfe4c_row10_col2\" class=\"data row10 col2\" ></td>\n",
       "      <td id=\"T_bfe4c_row10_col3\" class=\"data row10 col3\" >-1.24%</td>\n",
       "      <td id=\"T_bfe4c_row10_col4\" class=\"data row10 col4\" >1.28%</td>\n",
       "      <td id=\"T_bfe4c_row10_col5\" class=\"data row10 col5\" >-0.96%</td>\n",
       "      <td id=\"T_bfe4c_row10_col6\" class=\"data row10 col6\" >-0.65%</td>\n",
       "      <td id=\"T_bfe4c_row10_col7\" class=\"data row10 col7\" >-1.70%</td>\n",
       "      <td id=\"T_bfe4c_row10_col8\" class=\"data row10 col8\" >2.44%</td>\n",
       "      <td id=\"T_bfe4c_row10_col9\" class=\"data row10 col9\" >5.86%</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th id=\"T_bfe4c_level0_row11\" class=\"row_heading level0 row11\" >10.540000</th>\n",
       "      <td id=\"T_bfe4c_row11_col0\" class=\"data row11 col0\" ></td>\n",
       "      <td id=\"T_bfe4c_row11_col1\" class=\"data row11 col1\" ></td>\n",
       "      <td id=\"T_bfe4c_row11_col2\" class=\"data row11 col2\" >-0.84%</td>\n",
       "      <td id=\"T_bfe4c_row11_col3\" class=\"data row11 col3\" >-1.80%</td>\n",
       "      <td id=\"T_bfe4c_row11_col4\" class=\"data row11 col4\" >0.74%</td>\n",
       "      <td id=\"T_bfe4c_row11_col5\" class=\"data row11 col5\" >1.27%</td>\n",
       "      <td id=\"T_bfe4c_row11_col6\" class=\"data row11 col6\" >-0.03%</td>\n",
       "      <td id=\"T_bfe4c_row11_col7\" class=\"data row11 col7\" >1.25%</td>\n",
       "      <td id=\"T_bfe4c_row11_col8\" class=\"data row11 col8\" >4.20%</td>\n",
       "      <td id=\"T_bfe4c_row11_col9\" class=\"data row11 col9\" >5.06%</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th id=\"T_bfe4c_level0_row12\" class=\"row_heading level0 row12\" >14.760000</th>\n",
       "      <td id=\"T_bfe4c_row12_col0\" class=\"data row12 col0\" ></td>\n",
       "      <td id=\"T_bfe4c_row12_col1\" class=\"data row12 col1\" >-3.37%</td>\n",
       "      <td id=\"T_bfe4c_row12_col2\" class=\"data row12 col2\" >-2.68%</td>\n",
       "      <td id=\"T_bfe4c_row12_col3\" class=\"data row12 col3\" >-0.43%</td>\n",
       "      <td id=\"T_bfe4c_row12_col4\" class=\"data row12 col4\" >0.49%</td>\n",
       "      <td id=\"T_bfe4c_row12_col5\" class=\"data row12 col5\" >1.78%</td>\n",
       "      <td id=\"T_bfe4c_row12_col6\" class=\"data row12 col6\" >2.08%</td>\n",
       "      <td id=\"T_bfe4c_row12_col7\" class=\"data row12 col7\" >0.57%</td>\n",
       "      <td id=\"T_bfe4c_row12_col8\" class=\"data row12 col8\" >3.99%</td>\n",
       "      <td id=\"T_bfe4c_row12_col9\" class=\"data row12 col9\" >2.98%</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th id=\"T_bfe4c_level0_row13\" class=\"row_heading level0 row13\" >20.660000</th>\n",
       "      <td id=\"T_bfe4c_row13_col0\" class=\"data row13 col0\" ></td>\n",
       "      <td id=\"T_bfe4c_row13_col1\" class=\"data row13 col1\" >-4.86%</td>\n",
       "      <td id=\"T_bfe4c_row13_col2\" class=\"data row13 col2\" >-2.52%</td>\n",
       "      <td id=\"T_bfe4c_row13_col3\" class=\"data row13 col3\" >-1.21%</td>\n",
       "      <td id=\"T_bfe4c_row13_col4\" class=\"data row13 col4\" >1.57%</td>\n",
       "      <td id=\"T_bfe4c_row13_col5\" class=\"data row13 col5\" >3.32%</td>\n",
       "      <td id=\"T_bfe4c_row13_col6\" class=\"data row13 col6\" >2.52%</td>\n",
       "      <td id=\"T_bfe4c_row13_col7\" class=\"data row13 col7\" >4.23%</td>\n",
       "      <td id=\"T_bfe4c_row13_col8\" class=\"data row13 col8\" >4.25%</td>\n",
       "      <td id=\"T_bfe4c_row13_col9\" class=\"data row13 col9\" >4.56%</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th id=\"T_bfe4c_level0_row14\" class=\"row_heading level0 row14\" >28.930000</th>\n",
       "      <td id=\"T_bfe4c_row14_col0\" class=\"data row14 col0\" ></td>\n",
       "      <td id=\"T_bfe4c_row14_col1\" class=\"data row14 col1\" >-4.14%</td>\n",
       "      <td id=\"T_bfe4c_row14_col2\" class=\"data row14 col2\" >-2.41%</td>\n",
       "      <td id=\"T_bfe4c_row14_col3\" class=\"data row14 col3\" >0.32%</td>\n",
       "      <td id=\"T_bfe4c_row14_col4\" class=\"data row14 col4\" >1.70%</td>\n",
       "      <td id=\"T_bfe4c_row14_col5\" class=\"data row14 col5\" >3.74%</td>\n",
       "      <td id=\"T_bfe4c_row14_col6\" class=\"data row14 col6\" >4.10%</td>\n",
       "      <td id=\"T_bfe4c_row14_col7\" class=\"data row14 col7\" >2.50%</td>\n",
       "      <td id=\"T_bfe4c_row14_col8\" class=\"data row14 col8\" >4.16%</td>\n",
       "      <td id=\"T_bfe4c_row14_col9\" class=\"data row14 col9\" >5.46%</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th id=\"T_bfe4c_level0_row15\" class=\"row_heading level0 row15\" >40.500000</th>\n",
       "      <td id=\"T_bfe4c_row15_col0\" class=\"data row15 col0\" >-5.77%</td>\n",
       "      <td id=\"T_bfe4c_row15_col1\" class=\"data row15 col1\" >-3.10%</td>\n",
       "      <td id=\"T_bfe4c_row15_col2\" class=\"data row15 col2\" >-2.29%</td>\n",
       "      <td id=\"T_bfe4c_row15_col3\" class=\"data row15 col3\" >-0.70%</td>\n",
       "      <td id=\"T_bfe4c_row15_col4\" class=\"data row15 col4\" >3.04%</td>\n",
       "      <td id=\"T_bfe4c_row15_col5\" class=\"data row15 col5\" >1.15%</td>\n",
       "      <td id=\"T_bfe4c_row15_col6\" class=\"data row15 col6\" >0.28%</td>\n",
       "      <td id=\"T_bfe4c_row15_col7\" class=\"data row15 col7\" >4.78%</td>\n",
       "      <td id=\"T_bfe4c_row15_col8\" class=\"data row15 col8\" >3.37%</td>\n",
       "      <td id=\"T_bfe4c_row15_col9\" class=\"data row15 col9\" ></td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th id=\"T_bfe4c_level0_row16\" class=\"row_heading level0 row16\" >56.690000</th>\n",
       "      <td id=\"T_bfe4c_row16_col0\" class=\"data row16 col0\" ></td>\n",
       "      <td id=\"T_bfe4c_row16_col1\" class=\"data row16 col1\" >-3.75%</td>\n",
       "      <td id=\"T_bfe4c_row16_col2\" class=\"data row16 col2\" >-1.95%</td>\n",
       "      <td id=\"T_bfe4c_row16_col3\" class=\"data row16 col3\" >-0.56%</td>\n",
       "      <td id=\"T_bfe4c_row16_col4\" class=\"data row16 col4\" >3.48%</td>\n",
       "      <td id=\"T_bfe4c_row16_col5\" class=\"data row16 col5\" >2.48%</td>\n",
       "      <td id=\"T_bfe4c_row16_col6\" class=\"data row16 col6\" >0.35%</td>\n",
       "      <td id=\"T_bfe4c_row16_col7\" class=\"data row16 col7\" >4.71%</td>\n",
       "      <td id=\"T_bfe4c_row16_col8\" class=\"data row16 col8\" ></td>\n",
       "      <td id=\"T_bfe4c_row16_col9\" class=\"data row16 col9\" ></td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th id=\"T_bfe4c_level0_row17\" class=\"row_heading level0 row17\" >79.370000</th>\n",
       "      <td id=\"T_bfe4c_row17_col0\" class=\"data row17 col0\" >-7.36%</td>\n",
       "      <td id=\"T_bfe4c_row17_col1\" class=\"data row17 col1\" >-4.98%</td>\n",
       "      <td id=\"T_bfe4c_row17_col2\" class=\"data row17 col2\" >-1.80%</td>\n",
       "      <td id=\"T_bfe4c_row17_col3\" class=\"data row17 col3\" >-3.42%</td>\n",
       "      <td id=\"T_bfe4c_row17_col4\" class=\"data row17 col4\" >2.31%</td>\n",
       "      <td id=\"T_bfe4c_row17_col5\" class=\"data row17 col5\" >2.60%</td>\n",
       "      <td id=\"T_bfe4c_row17_col6\" class=\"data row17 col6\" >-1.99%</td>\n",
       "      <td id=\"T_bfe4c_row17_col7\" class=\"data row17 col7\" ></td>\n",
       "      <td id=\"T_bfe4c_row17_col8\" class=\"data row17 col8\" ></td>\n",
       "      <td id=\"T_bfe4c_row17_col9\" class=\"data row17 col9\" ></td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th id=\"T_bfe4c_level0_row18\" class=\"row_heading level0 row18\" >111.120000</th>\n",
       "      <td id=\"T_bfe4c_row18_col0\" class=\"data row18 col0\" >-10.11%</td>\n",
       "      <td id=\"T_bfe4c_row18_col1\" class=\"data row18 col1\" >-4.04%</td>\n",
       "      <td id=\"T_bfe4c_row18_col2\" class=\"data row18 col2\" >-4.50%</td>\n",
       "      <td id=\"T_bfe4c_row18_col3\" class=\"data row18 col3\" >-2.53%</td>\n",
       "      <td id=\"T_bfe4c_row18_col4\" class=\"data row18 col4\" >-0.71%</td>\n",
       "      <td id=\"T_bfe4c_row18_col5\" class=\"data row18 col5\" ></td>\n",
       "      <td id=\"T_bfe4c_row18_col6\" class=\"data row18 col6\" ></td>\n",
       "      <td id=\"T_bfe4c_row18_col7\" class=\"data row18 col7\" ></td>\n",
       "      <td id=\"T_bfe4c_row18_col8\" class=\"data row18 col8\" ></td>\n",
       "      <td id=\"T_bfe4c_row18_col9\" class=\"data row18 col9\" ></td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th id=\"T_bfe4c_level0_row19\" class=\"row_heading level0 row19\" >155.570000</th>\n",
       "      <td id=\"T_bfe4c_row19_col0\" class=\"data row19 col0\" ></td>\n",
       "      <td id=\"T_bfe4c_row19_col1\" class=\"data row19 col1\" ></td>\n",
       "      <td id=\"T_bfe4c_row19_col2\" class=\"data row19 col2\" >-3.03%</td>\n",
       "      <td id=\"T_bfe4c_row19_col3\" class=\"data row19 col3\" >-2.05%</td>\n",
       "      <td id=\"T_bfe4c_row19_col4\" class=\"data row19 col4\" ></td>\n",
       "      <td id=\"T_bfe4c_row19_col5\" class=\"data row19 col5\" ></td>\n",
       "      <td id=\"T_bfe4c_row19_col6\" class=\"data row19 col6\" ></td>\n",
       "      <td id=\"T_bfe4c_row19_col7\" class=\"data row19 col7\" ></td>\n",
       "      <td id=\"T_bfe4c_row19_col8\" class=\"data row19 col8\" ></td>\n",
       "      <td id=\"T_bfe4c_row19_col9\" class=\"data row19 col9\" ></td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n"
      ],
      "text/plain": [
       "<pandas.io.formats.style.Styler at 0x2b0cb72b0>"
      ]
     },
     "execution_count": 15,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "B_W_Metric_raw = dataset[['difficulty', 'stability', 'p', 'y']].copy()\n",
    "B_W_Metric_raw['s_bin'] = B_W_Metric_raw['stability'].map(lambda x: round(math.pow(1.4, math.floor(math.log(x, 1.4))), 2))\n",
    "B_W_Metric_raw['d_bin'] = B_W_Metric_raw['difficulty'].map(lambda x: int(round(x)))\n",
    "B_W_Metric = B_W_Metric_raw.groupby(by=['s_bin', 'd_bin']).agg('mean').reset_index()\n",
    "B_W_Metric_count = B_W_Metric_raw.groupby(by=['s_bin', 'd_bin']).agg('count').reset_index()\n",
    "B_W_Metric['B-W'] = B_W_Metric['p'] - B_W_Metric['y']\n",
    "n = len(dataset)\n",
    "bins = len(B_W_Metric)\n",
    "B_W_Metric_pivot = B_W_Metric[B_W_Metric_count['p'] > max(50, n / (3 * bins))].pivot(index=\"s_bin\", columns='d_bin', values='B-W')\n",
    "B_W_Metric_pivot.apply(pd.to_numeric).style.background_gradient(cmap='seismic', axis=None, vmin=-0.2, vmax=0.2).format(\"{:.2%}\", na_rep='')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "DiffMax: 0.1523328046523923\n"
     ]
    },
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
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       "\n",
       "    .dataframe tbody tr th {\n",
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       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>S_rounded</th>\n",
       "      <th>D_rounded</th>\n",
       "      <th>R_t_rounded</th>\n",
       "      <th>R_measured</th>\n",
       "      <th>count</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>12</th>\n",
       "      <td>0.26</td>\n",
       "      <td>10</td>\n",
       "      <td>0.725</td>\n",
       "      <td>0.780379</td>\n",
       "      <td>897</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>47</th>\n",
       "      <td>0.36</td>\n",
       "      <td>10</td>\n",
       "      <td>0.775</td>\n",
       "      <td>0.790000</td>\n",
       "      <td>1100</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>48</th>\n",
       "      <td>0.36</td>\n",
       "      <td>10</td>\n",
       "      <td>0.800</td>\n",
       "      <td>0.874435</td>\n",
       "      <td>1107</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>100</th>\n",
       "      <td>0.51</td>\n",
       "      <td>10</td>\n",
       "      <td>0.825</td>\n",
       "      <td>0.889391</td>\n",
       "      <td>1329</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>101</th>\n",
       "      <td>0.51</td>\n",
       "      <td>10</td>\n",
       "      <td>0.850</td>\n",
       "      <td>0.894059</td>\n",
       "      <td>1397</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1629</th>\n",
       "      <td>56.69</td>\n",
       "      <td>5</td>\n",
       "      <td>0.950</td>\n",
       "      <td>0.894191</td>\n",
       "      <td>482</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1727</th>\n",
       "      <td>79.37</td>\n",
       "      <td>2</td>\n",
       "      <td>0.900</td>\n",
       "      <td>0.960219</td>\n",
       "      <td>729</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1739</th>\n",
       "      <td>79.37</td>\n",
       "      <td>3</td>\n",
       "      <td>0.900</td>\n",
       "      <td>0.930314</td>\n",
       "      <td>574</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1751</th>\n",
       "      <td>79.37</td>\n",
       "      <td>4</td>\n",
       "      <td>0.875</td>\n",
       "      <td>0.937349</td>\n",
       "      <td>415</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1754</th>\n",
       "      <td>79.37</td>\n",
       "      <td>4</td>\n",
       "      <td>0.950</td>\n",
       "      <td>0.942424</td>\n",
       "      <td>330</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>180 rows × 5 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "      S_rounded  D_rounded  R_t_rounded  R_measured  count\n",
       "12         0.26         10        0.725    0.780379    897\n",
       "47         0.36         10        0.775    0.790000   1100\n",
       "48         0.36         10        0.800    0.874435   1107\n",
       "100        0.51         10        0.825    0.889391   1329\n",
       "101        0.51         10        0.850    0.894059   1397\n",
       "...         ...        ...          ...         ...    ...\n",
       "1629      56.69          5        0.950    0.894191    482\n",
       "1727      79.37          2        0.900    0.960219    729\n",
       "1739      79.37          3        0.900    0.930314    574\n",
       "1751      79.37          4        0.875    0.937349    415\n",
       "1754      79.37          4        0.950    0.942424    330\n",
       "\n",
       "[180 rows x 5 columns]"
      ]
     },
     "execution_count": 16,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "B_W_Metric_raw['r_bin'] = B_W_Metric_raw['p'].map(lambda x: round(x * 4, 1) / 4)\n",
    "R_Matrix = B_W_Metric_raw.groupby(by=['s_bin', 'd_bin', 'r_bin']).agg({'y': ['mean', 'count']}).reset_index()\n",
    "R_Matrix.columns = ['S_rounded', 'D_rounded', 'R_t_rounded', 'R_measured', 'count']\n",
    "R_Matrix = R_Matrix[R_Matrix['count'] > 300]\n",
    "print(\"DiffMax:\", max(abs(R_Matrix['R_t_rounded'] - R_Matrix['R_measured'])))\n",
    "R_Matrix"
   ]
  },
  {
   "attachments": {},
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### 4.3 Comparision with Anki bulit-in algorithm"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {},
   "outputs": [],
   "source": [
    "def anki(history):\n",
    "    ivl = 0\n",
    "    start_ease = 2.5\n",
    "    hard_interval = 1.2\n",
    "    ease_bonus = 1.3\n",
    "    graduating_interval = 1\n",
    "    easy_interval = 4\n",
    "    new_interval = 0\n",
    "    minimum_interval = 1\n",
    "    interval_modifier = 1\n",
    "    for i, (delta_t, rating) in enumerate(history):\n",
    "        delta_t = delta_t.item()\n",
    "        rating = rating.item()\n",
    "        if i == 0:\n",
    "            ease = start_ease\n",
    "            if rating == 4:\n",
    "                ivl = easy_interval\n",
    "            else:\n",
    "                ivl = graduating_interval\n",
    "        else:\n",
    "            delay = delta_t - ivl\n",
    "            if delay >= 0:\n",
    "                if rating == 1:\n",
    "                    ivl = max(ivl * new_interval, minimum_interval)\n",
    "                    ease -= 0.2\n",
    "                elif rating == 2:\n",
    "                    ivl = ivl * hard_interval\n",
    "                    ease -= 0.15\n",
    "                elif rating == 3:\n",
    "                    ivl = (ivl + delay // 2) * ease\n",
    "                else:\n",
    "                    ivl = (ivl + delay) * ease * ease_bonus\n",
    "                    ease += 0.15\n",
    "            # early_review\n",
    "            else:\n",
    "                if rating == 1:\n",
    "                    ivl = max(ivl * new_interval, minimum_interval)\n",
    "                    ease -= 0.2\n",
    "                elif rating == 2:\n",
    "                    ivl = max(delta_t * hard_interval / 2, ivl * hard_interval)\n",
    "                    ease -= 0.15\n",
    "                elif rating == 3:\n",
    "                    ivl = max(delta_t * ease, ivl)\n",
    "                else:\n",
    "                    ivl = max(delta_t * ease, ivl) * (0.5 * ease_bonus + 0.5)\n",
    "                    ease += 0.15\n",
    "            ivl = ivl * interval_modifier\n",
    "        ease = max(1.3, ease)\n",
    "        ivl = max(1, round(ivl+0.01))\n",
    "    return ivl\n",
    "\n",
    "def sm2(history):\n",
    "    ivl = 0\n",
    "    ef = 2.5\n",
    "    reps = 0\n",
    "    for delta_t, rating in history:\n",
    "        delta_t = delta_t.item()\n",
    "        rating = rating.item() + 1\n",
    "        if rating > 2:\n",
    "            if reps == 0:\n",
    "                ivl = 1\n",
    "                reps = 1\n",
    "            elif reps == 1:\n",
    "                ivl = 6\n",
    "                reps = 2\n",
    "            else:\n",
    "                ivl = ivl * ef\n",
    "                reps += 1\n",
    "        else:\n",
    "            ivl = 1\n",
    "            reps = 0\n",
    "        ef = max(1.3, ef + (0.1 - (5 - rating) * (0.08 + (5 - rating) * 0.02)))\n",
    "        ivl = max(1, round(ivl+0.01))\n",
    "    return ivl\n",
    "\n",
    "dataset['sm2_interval'] = dataset['tensor'].map(sm2)\n",
    "dataset['sm2_p'] = np.exp(np.log(0.9) * dataset['delta_t'] / dataset['sm2_interval'])\n",
    "cross_comparison = dataset[['sm2_p', 'p', 'y']].copy()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "R_Metric:  0.03340962095346084\n",
      "R-squared: -21.5980\n",
      "RMSE: 0.1408\n",
      "[0.76478601 0.15818509]\n"
     ]
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 640x480 with 2 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "R_Metric = mean_squared_error(cross_comparison['y'], cross_comparison['sm2_p'], squared=False) - mean_squared_error(cross_comparison['y'], cross_comparison['p'], squared=False)\n",
    "print(\"R_Metric: \", R_Metric)\n",
    "plot_brier(dataset['sm2_p'], dataset['y'], bins=40)\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Universal Metric of FSRS: 0.0183\n",
      "Universal Metric of SM2: 0.0717\n"
     ]
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 600x600 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.figure(figsize=(6, 6))\n",
    "\n",
    "cross_comparison['SM2_B-W'] = cross_comparison['sm2_p'] - cross_comparison['y']\n",
    "cross_comparison['SM2_bin'] = cross_comparison['sm2_p'].map(lambda x: round(x, 1))\n",
    "cross_comparison['FSRS_B-W'] = cross_comparison['p'] - cross_comparison['y']\n",
    "cross_comparison['FSRS_bin'] = cross_comparison['p'].map(lambda x: round(x, 1))\n",
    "\n",
    "plt.axhline(y = 0.0, color = 'black', linestyle = '-')\n",
    "\n",
    "cross_comparison_group = cross_comparison.groupby(by='SM2_bin').agg({'y': ['mean'], 'FSRS_B-W': ['mean'], 'p': ['mean', 'count']})\n",
    "print(f\"Universal Metric of FSRS: {mean_squared_error(cross_comparison_group['y', 'mean'], cross_comparison_group['p', 'mean'], sample_weight=cross_comparison_group['p', 'count'], squared=False):.4f}\")\n",
    "cross_comparison_group['p', 'percent'] = cross_comparison_group['p', 'count'] / cross_comparison_group['p', 'count'].sum()\n",
    "plt.scatter(cross_comparison_group.index, cross_comparison_group['FSRS_B-W', 'mean'], s=cross_comparison_group['p', 'percent'] * 1024, alpha=0.5)\n",
    "plt.plot(cross_comparison_group['FSRS_B-W', 'mean'], label='FSRS by SM2')\n",
    "\n",
    "cross_comparison_group = cross_comparison.groupby(by='FSRS_bin').agg({'y': ['mean'], 'SM2_B-W': ['mean'], 'sm2_p': ['mean', 'count']})\n",
    "print(f\"Universal Metric of SM2: {mean_squared_error(cross_comparison_group['y', 'mean'], cross_comparison_group['sm2_p', 'mean'], sample_weight=cross_comparison_group['sm2_p', 'count'], squared=False):.4f}\")\n",
    "cross_comparison_group['sm2_p', 'percent'] = cross_comparison_group['sm2_p', 'count'] / cross_comparison_group['sm2_p', 'count'].sum()\n",
    "plt.scatter(cross_comparison_group.index, cross_comparison_group['SM2_B-W', 'mean'], s=cross_comparison_group['sm2_p', 'percent'] * 1024, alpha=0.5)\n",
    "plt.plot(cross_comparison_group['SM2_B-W', 'mean'], label='SM2 by FSRS')\n",
    "\n",
    "plt.legend(loc='lower center')\n",
    "plt.grid(linestyle='--')\n",
    "plt.title(\"SM2 vs. FSRS\")\n",
    "plt.xlabel('Predicted R')\n",
    "plt.ylabel('B-W Metric')\n",
    "plt.xlim(0, 1)\n",
    "plt.xticks(np.arange(0, 1.1, 0.1))\n",
    "plt.show()"
   ]
  }
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